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imaging_preprocess.py

activate(imaging_schema_name, scan_schema_name=None, *, create_schema=True, create_tables=True, linking_module=None)

Activate this schema.

Parameters:

Name Type Description Default
imaging_schema_name str

Schema name on the database server to activate the imaging module.

 required
scan_schema_name str

Schema name on the database server to activate the scan module. Omitted, if the scan module is already activated.

 None
create_schema bool

When True (default), create schema in the database if it does not yet exist.

 True
create_tables bool

When True (default), create tables in the database if they do not yet exist.

 True
linking_module str

A module name or a module containing the required dependencies to activate the imaging module: + all that are required by the scan module.

 None

Dependencies: Upstream tables: + Session: A parent table to Scan, identifying a scanning session. + Equipment: A parent table to Scan, identifying a scanning device.

Source code in element_calcium_imaging/imaging_preprocess.py 
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def activate(
    imaging_schema_name: str,
    scan_schema_name: str = None,
    *,
    create_schema: bool = True,
    create_tables: bool = True,
    linking_module: str = None,
):
    """Activate this schema.

    Args:
        imaging_schema_name (str): Schema name on the database server to activate the
            `imaging` module.
        scan_schema_name (str): Schema name on the database server to activate the
            `scan` module. Omitted, if the `scan` module is already activated.
        create_schema (bool): When True (default), create schema in the database if it
            does not yet exist.
        create_tables (bool): When True (default), create tables in the database if they
            do not yet exist.
        linking_module (str): A module name or a module containing the required
            dependencies to activate the `imaging` module: + all that are required by
            the `scan` module.

    Dependencies:
    Upstream tables:
        + Session: A parent table to Scan, identifying a scanning session.
        + Equipment: A parent table to Scan, identifying a scanning device.
    """

    if isinstance(linking_module, str):
        linking_module = importlib.import_module(linking_module)
    assert inspect.ismodule(
        linking_module
    ), "The argument 'dependency' must be a module's name or a module"

    global _linking_module
    _linking_module = linking_module

    scan.activate(
        scan_schema_name,
        create_schema=create_schema,
        create_tables=create_tables,
        linking_module=linking_module,
    )
    schema.activate(
        imaging_schema_name,
        create_schema=create_schema,
        create_tables=create_tables,
        add_objects=_linking_module.__dict__,
    )
    imaging_report.activate(f"{imaging_schema_name}_report", imaging_schema_name)

PreprocessMethod

Bases: Lookup

Method(s) used for preprocessing of calcium imaging data.

Attributes:

Name Type Description
preprocess_method str

Preprocessing method.
preprocess_method_desc str

Processing method description.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class PreprocessMethod(dj.Lookup):
    """Method(s) used for preprocessing of calcium imaging data.

    Attributes:
        preprocess_method (str): Preprocessing method.
        preprocess_method_desc (str): Processing method description.
    """

    definition = """  #  Method/package used for pre-processing
    preprocess_method: varchar(16)
    ---
    preprocess_method_desc: varchar(1000)
    """

PreprocessParamSet

Bases: Lookup

Parameter set used for the preprocessing of the calcium imaging scans.

A hash of the parameters of the analysis suite is also stored in order to avoid duplicated entries.

Attributes:

Name Type Description
paramset_idx int

Unique parameter set ID.
PreprocessMethod foreign key

A primary key from PreprocessMethod.
paramset_desc str

Parameter set description.
param_set_hash uuid

A universally unique identifier for the parameter set.
params longblob

Parameter Set, a dictionary of all applicable parameters to the analysis suite.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class PreprocessParamSet(dj.Lookup):
    """Parameter set used for the preprocessing of the calcium imaging scans.

    A hash of the parameters of the analysis suite is also stored in order
    to avoid duplicated entries.

    Attributes:
        paramset_idx (int): Unique parameter set ID.
        PreprocessMethod (foreign key): A primary key from PreprocessMethod.
        paramset_desc (str): Parameter set description.
        param_set_hash (uuid): A universally unique identifier for the parameter set.
        params (longblob): Parameter Set, a dictionary of all applicable parameters to
            the analysis suite.
    """

    definition = """  #  Parameter set used for pre-processing of calcium imaging data
    paramset_idx:  smallint
    ---
    -> PreprocessMethod
    paramset_desc: varchar(128)
    param_set_hash: uuid
    unique index (param_set_hash)
    params: longblob  # dictionary of all applicable parameters
    """

    @classmethod
    def insert_new_params(
        cls,
        preprocess_method: str,
        paramset_idx: int,
        paramset_desc: str,
        params: dict,
    ):
        """Insert a parameter set into PreprocessParamSet table.
        This function automates the parameter set hashing and avoids insertion of an
            existing parameter set.

        Attributes:
            preprocess_method (str): Method used for processing of calcium imaging scans.
            paramset_idx (int): Unique parameter set ID.
            paramset_desc (str): Parameter set description.
            params (dict): Parameter Set, all applicable parameters.
        """
        param_dict = {
            "preprocess_method": preprocess_method,
            "paramset_idx": paramset_idx,
            "paramset_desc": paramset_desc,
            "params": params,
            "param_set_hash": dict_to_uuid(params),
        }
        q_param = cls & {"param_set_hash": param_dict["param_set_hash"]}

        if q_param:  # If the specified param-set already exists
            p_name = q_param.fetch1("paramset_idx")
            if p_name == paramset_idx:  # If the existed set has the same name: job done
                return
            else:  # If not same name: human error, trying to add the same paramset with different name
                raise dj.DataJointError(
                    "The specified param-set already exists - name: {}".format(p_name)
                )
        else:
            cls.insert1(param_dict)

insert_new_params(preprocess_method, paramset_idx, paramset_desc, params) classmethod

Insert a parameter set into PreprocessParamSet table. This function automates the parameter set hashing and avoids insertion of an existing parameter set.

Attributes:

Name Type Description
preprocess_method str

Method used for processing of calcium imaging scans.
paramset_idx int

Unique parameter set ID.
paramset_desc str

Parameter set description.
params dict

Parameter Set, all applicable parameters.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@classmethod
def insert_new_params(
    cls,
    preprocess_method: str,
    paramset_idx: int,
    paramset_desc: str,
    params: dict,
):
    """Insert a parameter set into PreprocessParamSet table.
    This function automates the parameter set hashing and avoids insertion of an
        existing parameter set.

    Attributes:
        preprocess_method (str): Method used for processing of calcium imaging scans.
        paramset_idx (int): Unique parameter set ID.
        paramset_desc (str): Parameter set description.
        params (dict): Parameter Set, all applicable parameters.
    """
    param_dict = {
        "preprocess_method": preprocess_method,
        "paramset_idx": paramset_idx,
        "paramset_desc": paramset_desc,
        "params": params,
        "param_set_hash": dict_to_uuid(params),
    }
    q_param = cls & {"param_set_hash": param_dict["param_set_hash"]}

    if q_param:  # If the specified param-set already exists
        p_name = q_param.fetch1("paramset_idx")
        if p_name == paramset_idx:  # If the existed set has the same name: job done
            return
        else:  # If not same name: human error, trying to add the same paramset with different name
            raise dj.DataJointError(
                "The specified param-set already exists - name: {}".format(p_name)
            )
    else:
        cls.insert1(param_dict)

PreprocessParamSteps

Bases: Manual

Ordered list of paramset_idx that will be run.

When pre-processing is not performed, do not create an entry in Step Part table

Attributes:

Name Type Description
preprocess_param_steps_id int
preprocess_param_steps_name str
preprocess_param_steps_desc str
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class PreprocessParamSteps(dj.Manual):
    """Ordered list of paramset_idx that will be run.

    When pre-processing is not performed, do not create an entry in `Step` Part table

    Attributes:
        preprocess_param_steps_id (int):
        preprocess_param_steps_name (str):
        preprocess_param_steps_desc (str):
    """

    definition = """
    preprocess_param_steps_id: smallint
    ---
    preprocess_param_steps_name: varchar(32)
    preprocess_param_steps_desc: varchar(128)
    """

    class Step(dj.Part):
        """ADD DEFINITION

        Attributes:
            PreprocessParamSteps (foreign key): A primary key from PreprocessParamSteps.
            step_number (int):
            PreprocessParamSet (foreign key): A primary key from PreprocessParamSet.
        """

        definition = """
        -> master
        step_number: smallint                  # Order of operations
        ---
        -> PreprocessParamSet
        """

Step

Bases: Part

ADD DEFINITION

Attributes:

Name Type Description
PreprocessParamSteps foreign key

A primary key from PreprocessParamSteps.
step_number int
PreprocessParamSet foreign key

A primary key from PreprocessParamSet.
Source code in element_calcium_imaging/imaging_preprocess.py 
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class Step(dj.Part):
    """ADD DEFINITION

    Attributes:
        PreprocessParamSteps (foreign key): A primary key from PreprocessParamSteps.
        step_number (int):
        PreprocessParamSet (foreign key): A primary key from PreprocessParamSet.
    """

    definition = """
    -> master
    step_number: smallint                  # Order of operations
    ---
    -> PreprocessParamSet
    """

PreprocessTask

Bases: Manual

This table defines a calcium imaging preprocessing task for a combination of a Scan and a PreprocessParamSteps entries, including all the inputs (scan, method, steps). The task defined here is then run in the downstream table Preprocess. This table supports definitions of both loading of pre-generated, results, triggering of new analysis, or skipping of preprocessing step.

Attributes:

Name Type Description
Scan foreign key

A primary key from Scan.
PreprocessParamSteps foreign key

A primary key from PreprocessParamSteps.
preprocess_output_dir str

Output directory for the results of preprocessing.
task_mode str

One of 'load' (load computed analysis results), 'trigger' (trigger computation), 'none' (no pre-processing). Default none.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class PreprocessTask(dj.Manual):
    """This table defines a calcium imaging preprocessing task for a combination of a
    `Scan` and a `PreprocessParamSteps` entries, including all the inputs (scan, method,
    steps). The task defined here is then run in the downstream table
    Preprocess. This table supports definitions of both loading of pre-generated,
    results, triggering of new analysis, or skipping of preprocessing step.

    Attributes:
        Scan (foreign key): A primary key from Scan.
        PreprocessParamSteps (foreign key): A primary key from PreprocessParamSteps.
        preprocess_output_dir (str): Output directory for the results of preprocessing.
        task_mode (str, optional): One of 'load' (load computed analysis results), 'trigger'
            (trigger computation), 'none' (no pre-processing). Default none.
    """

    definition = """
    # Manual table for defining a pre-processing task ready to be run
    -> scan.Scan
    -> PreprocessParamSteps
    ---
    preprocess_output_dir: varchar(255)  # Pre-processing output directory relative
                                         # to the root data directory
    task_mode='none': enum('none','load', 'trigger') # 'none': no pre-processing
                                                     # 'load': load analysis results
                                                     # 'trigger': trigger computation
    """

Preprocess

Bases: Imported

Perform the computation of an entry (task) defined in the PreprocessTask table.

  • If task_mode == "none": no pre-processing performed
  • If task_mode == "trigger": Not implemented
  • If task_mode == "load": Not implemented

Attributes:

Name Type Description
PreprocessTask foreign key
preprocess_time datetime
package_version str

Version of the analysis package used in processing the data.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class Preprocess(dj.Imported):
    """Perform the computation of an entry (task) defined in the PreprocessTask table.

    + If `task_mode == "none"`: no pre-processing performed
    + If `task_mode == "trigger"`: Not implemented
    + If `task_mode == "load"`: Not implemented

    Attributes:
        PreprocessTask (foreign key):
        preprocess_time (datetime, optional):
        package_version (str, optional): Version of the analysis package used in
            processing the data.
    """

    definition = """
    -> PreprocessTask
    ---
    preprocess_time=null: datetime  # Time of generation of pre-processing results
    package_version='': varchar(16)
    """

    def make(self, key):
        """Execute the preprocessing analysis steps defined in PreprocessTask."""

        task_mode, output_dir = (PreprocessTask & key).fetch1(
            "task_mode", "preprocess_output_dir"
        )
        _ = find_full_path(get_imaging_root_data_dir(), output_dir)

        if task_mode == "none":
            print(f"No pre-processing run on entry: {key}")
        elif task_mode in ["load", "trigger"]:
            raise NotImplementedError(
                "Pre-processing steps are not implemented."
                "Please overwrite this `make` function with"
                "desired pre-processing steps."
            )
        else:
            raise ValueError(f"Unknown task mode: {task_mode}")

        self.insert1({**key, "package_version": ""})

make(key)

Execute the preprocessing analysis steps defined in PreprocessTask.

Source code in element_calcium_imaging/imaging_preprocess.py 
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def make(self, key):
    """Execute the preprocessing analysis steps defined in PreprocessTask."""

    task_mode, output_dir = (PreprocessTask & key).fetch1(
        "task_mode", "preprocess_output_dir"
    )
    _ = find_full_path(get_imaging_root_data_dir(), output_dir)

    if task_mode == "none":
        print(f"No pre-processing run on entry: {key}")
    elif task_mode in ["load", "trigger"]:
        raise NotImplementedError(
            "Pre-processing steps are not implemented."
            "Please overwrite this `make` function with"
            "desired pre-processing steps."
        )
    else:
        raise ValueError(f"Unknown task mode: {task_mode}")

    self.insert1({**key, "package_version": ""})

ProcessingMethod

Bases: Lookup

Package used for processing of calcium imaging data (e.g. Suite2p, CaImAn, etc.).

Attributes:

Name Type Description
processing_method str

Processing method.
processing_method_desc str

Processing method description.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class ProcessingMethod(dj.Lookup):
    """Package used for processing of calcium imaging data (e.g. Suite2p, CaImAn, etc.).

    Attributes:
        processing_method (str): Processing method.
        processing_method_desc (str): Processing method description.
    """

    definition = """# Package used for processing of calcium imaging data (e.g. Suite2p, CaImAn, etc.).
    processing_method: char(8)
    ---
    processing_method_desc: varchar(1000)  # Processing method description
    """

    contents = [
        ("suite2p", "suite2p analysis suite"),
        ("caiman", "caiman analysis suite"),
        ("extract", "extract analysis suite"),
    ]

ProcessingParamSet

Bases: Lookup

Parameter set used for the processing of the calcium imaging scans, including both the analysis suite and its respective input parameters.

A hash of the parameters of the analysis suite is also stored in order to avoid duplicated entries.

Attributes:

Name Type Description
paramset_idx int

Unique parameter set ID.
ProcessingMethod foreign key

A primary key from ProcessingMethod.
paramset_desc str

Parameter set description.
param_set_hash uuid

A universally unique identifier for the parameter set.
params longblob

Parameter Set, a dictionary of all applicable parameters to the analysis suite.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class ProcessingParamSet(dj.Lookup):
    """Parameter set used for the processing of the calcium imaging scans,
    including both the analysis suite and its respective input parameters.

    A hash of the parameters of the analysis suite is also stored in order
    to avoid duplicated entries.

    Attributes:
        paramset_idx (int): Unique parameter set ID.
        ProcessingMethod (foreign key): A primary key from ProcessingMethod.
        paramset_desc (str): Parameter set description.
        param_set_hash (uuid): A universally unique identifier for the parameter set.
        params (longblob): Parameter Set, a dictionary of all applicable parameters to
            the analysis suite.
    """

    definition = """# Processing Parameter Set
    paramset_idx: smallint  # Unique parameter set ID.
    ---
    -> ProcessingMethod
    paramset_desc: varchar(1280)  # Parameter-set description
    param_set_hash: uuid  # A universally unique identifier for the parameter set
    unique index (param_set_hash)
    params: longblob  # Parameter Set, a dictionary of all applicable parameters to the analysis suite.
    """

    @classmethod
    def insert_new_params(
        cls,
        processing_method: str,
        paramset_idx: int,
        paramset_desc: str,
        params: dict,
    ):
        """Insert a parameter set into ProcessingParamSet table.

        This function automates the parameter set hashing and avoids insertion of an
            existing parameter set.

        Attributes:
            processing_method (str): Processing method/package used for processing of
                calcium imaging.
            paramset_idx (int): Unique parameter set ID.
            paramset_desc (str): Parameter set description.
            params (dict): Parameter Set, all applicable parameters to the analysis
                suite.
        """
        if processing_method == "extract":
            assert (
                params.get("extract") is not None and params.get("suite2p") is not None
            ), ValueError(
                "Please provide the processing parameters in the {'suite2p': {...}, 'extract': {...}} dictionary format."
            )

            # Force Suite2p to only run motion correction.
            params["suite2p"]["do_registration"] = True
            params["suite2p"]["roidetect"] = False

        param_dict = {
            "processing_method": processing_method,
            "paramset_idx": paramset_idx,
            "paramset_desc": paramset_desc,
            "params": params,
            "param_set_hash": dict_to_uuid(params),
        }
        q_param = cls & {"param_set_hash": param_dict["param_set_hash"]}

        if q_param:  # If the specified param-set already exists
            p_name = q_param.fetch1("paramset_idx")
            if p_name == paramset_idx:  # If the existed set has the same name: job done
                return
            else:  # If not same name: human error, trying to add the same paramset with different name
                raise dj.DataJointError(
                    "The specified param-set already exists - name: {}".format(p_name)
                )
        else:
            cls.insert1(param_dict)

insert_new_params(processing_method, paramset_idx, paramset_desc, params) classmethod

Insert a parameter set into ProcessingParamSet table.

This function automates the parameter set hashing and avoids insertion of an existing parameter set.

Attributes:

Name Type Description
processing_method str

Processing method/package used for processing of calcium imaging.
paramset_idx int

Unique parameter set ID.
paramset_desc str

Parameter set description.
params dict

Parameter Set, all applicable parameters to the analysis suite.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@classmethod
def insert_new_params(
    cls,
    processing_method: str,
    paramset_idx: int,
    paramset_desc: str,
    params: dict,
):
    """Insert a parameter set into ProcessingParamSet table.

    This function automates the parameter set hashing and avoids insertion of an
        existing parameter set.

    Attributes:
        processing_method (str): Processing method/package used for processing of
            calcium imaging.
        paramset_idx (int): Unique parameter set ID.
        paramset_desc (str): Parameter set description.
        params (dict): Parameter Set, all applicable parameters to the analysis
            suite.
    """
    if processing_method == "extract":
        assert (
            params.get("extract") is not None and params.get("suite2p") is not None
        ), ValueError(
            "Please provide the processing parameters in the {'suite2p': {...}, 'extract': {...}} dictionary format."
        )

        # Force Suite2p to only run motion correction.
        params["suite2p"]["do_registration"] = True
        params["suite2p"]["roidetect"] = False

    param_dict = {
        "processing_method": processing_method,
        "paramset_idx": paramset_idx,
        "paramset_desc": paramset_desc,
        "params": params,
        "param_set_hash": dict_to_uuid(params),
    }
    q_param = cls & {"param_set_hash": param_dict["param_set_hash"]}

    if q_param:  # If the specified param-set already exists
        p_name = q_param.fetch1("paramset_idx")
        if p_name == paramset_idx:  # If the existed set has the same name: job done
            return
        else:  # If not same name: human error, trying to add the same paramset with different name
            raise dj.DataJointError(
                "The specified param-set already exists - name: {}".format(p_name)
            )
    else:
        cls.insert1(param_dict)

CellCompartment

Bases: Lookup

Cell compartments that can be imaged (e.g. 'axon', 'soma', etc.)

Attributes:

Name Type Description
cell_compartment str

Cell compartment.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class CellCompartment(dj.Lookup):
    """Cell compartments that can be imaged (e.g. 'axon', 'soma', etc.)

    Attributes:
        cell_compartment (str): Cell compartment.
    """

    definition = """# Cell compartments
    cell_compartment: char(16)
    """

    contents = zip(["axon", "soma", "bouton"])

MaskType

Bases: Lookup

Available labels for segmented masks (e.g. 'soma', 'axon', 'dendrite', 'neuropil').

Attributes:

Name Type Description
mask_type str

Mask type.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class MaskType(dj.Lookup):
    """Available labels for segmented masks (e.g. 'soma', 'axon', 'dendrite', 'neuropil').

    Attributes:
        mask_type (str): Mask type.
    """

    definition = """# Possible types of a segmented mask
    mask_type: varchar(16)
    """

    contents = zip(["soma", "axon", "dendrite", "neuropil", "artefact", "unknown"])

ProcessingTask

Bases: Manual

A pairing of processing params and scans to be loaded or triggered

This table defines a calcium imaging processing task for a combination of a Scan and a ProcessingParamSet entries, including all the inputs (scan, method, method's parameters). The task defined here is then run in the downstream table Processing. This table supports definitions of both loading of pre-generated results and the triggering of new analysis for all supported analysis methods.

Attributes:

Name Type Description
Preprocess foreign key

Primary key from Preprocess.
ProcessingParamSet foreign key

Primary key from ProcessingParamSet.
processing_output_dir str

Output directory of the processed scan relative to the root data directory.
task_mode str

One of 'load' (load computed analysis results) or 'trigger' (trigger computation).
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class ProcessingTask(dj.Manual):
    """A pairing of processing params and scans to be loaded or triggered

    This table defines a calcium imaging processing task for a combination of a
    `Scan` and a `ProcessingParamSet` entries, including all the inputs (scan, method,
    method's parameters). The task defined here is then run in the downstream table
    `Processing`. This table supports definitions of both loading of pre-generated results
    and the triggering of new analysis for all supported analysis methods.

    Attributes:
        Preprocess (foreign key): Primary key from Preprocess.
        ProcessingParamSet (foreign key): Primary key from ProcessingParamSet.
        processing_output_dir (str): Output directory of the processed scan relative to the root data directory.
        task_mode (str): One of 'load' (load computed analysis results) or 'trigger'
            (trigger computation).
    """

    definition = """# Manual table for defining a processing task ready to be run
    -> Preprocess
    -> ProcessingParamSet
    ---
    processing_output_dir: varchar(255)  #  Output directory of the processed scan relative to root data directory
    task_mode='load': enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
    """

    @classmethod
    def infer_output_dir(cls, key, relative=False, mkdir=False):
        """Infer an output directory for an entry in ProcessingTask table.

        Args:
            key (dict): Primary key from the ProcessingTask table.
            relative (bool): If True, processing_output_dir is returned relative to
                imaging_root_dir. Default False.
            mkdir (bool): If True, create the processing_output_dir directory.
                Default True.

        Returns:
            dir (str): A default output directory for the processed results (processed_output_dir
                in ProcessingTask) based on the following convention:
                processed_dir / scan_dir / {processing_method}_{paramset_idx}
                e.g.: sub4/sess1/scan0/suite2p_0
        """
        acq_software = (scan.Scan & key).fetch1("acq_software")
        filetypes = dict(
            ScanImage="*.tif", Scanbox="*.sbx", NIS="*.nd2", PrairieView="*.tif"
        )

        scan_dir = find_full_path(
            get_imaging_root_data_dir(),
            get_calcium_imaging_files(key, filetypes[acq_software])[0],
        ).parent
        root_dir = find_root_directory(get_imaging_root_data_dir(), scan_dir)

        method = (
            (ProcessingParamSet & key).fetch1("processing_method").replace(".", "-")
        )

        processed_dir = pathlib.Path(get_processed_root_data_dir())
        output_dir = (
            processed_dir
            / scan_dir.relative_to(root_dir)
            / f'{method}_{key["paramset_idx"]}'
        )

        if mkdir:
            output_dir.mkdir(parents=True, exist_ok=True)

        return output_dir.relative_to(processed_dir) if relative else output_dir

    @classmethod
    def generate(cls, scan_key, paramset_idx=0):
        """Generate a ProcessingTask for a Scan using an parameter ProcessingParamSet

        Generate an entry in the ProcessingTask table for a particular scan using an
        existing parameter set from the ProcessingParamSet table.

        Args:
            scan_key (dict): Primary key from Scan table.
            paramset_idx (int): Unique parameter set ID.
        """
        key = {**scan_key, "paramset_idx": paramset_idx}

        processed_dir = get_processed_root_data_dir()
        output_dir = cls.infer_output_dir(key, relative=False, mkdir=True)

        method = (ProcessingParamSet & {"paramset_idx": paramset_idx}).fetch1(
            "processing_method"
        )

        try:
            if method == "suite2p":
                from element_interface import suite2p_loader

                suite2p_loader.Suite2p(output_dir)
            elif method == "caiman":
                from element_interface import caiman_loader

                caiman_loader.CaImAn(output_dir)
            elif method == "extract":
                from element_interface import extract_loader

                extract_loader.EXTRACT(output_dir)

            else:
                raise NotImplementedError(
                    "Unknown/unimplemented method: {}".format(method)
                )
        except FileNotFoundError:
            task_mode = "trigger"
        else:
            task_mode = "load"

        cls.insert1(
            {
                **key,
                "processing_output_dir": output_dir.relative_to(
                    processed_dir
                ).as_posix(),
                "task_mode": task_mode,
            }
        )

    auto_generate_entries = generate

infer_output_dir(key, relative=False, mkdir=False) classmethod

Infer an output directory for an entry in ProcessingTask table.

Parameters:

Name Type Description Default
key dict

Primary key from the ProcessingTask table.

 required
relative bool

If True, processing_output_dir is returned relative to imaging_root_dir. Default False.

 False
mkdir bool

If True, create the processing_output_dir directory. Default True.

 False

Returns:

Name Type Description
dir str

A default output directory for the processed results (processed_output_dir in ProcessingTask) based on the following convention: processed_dir / scan_dir / {processing_method}_{paramset_idx} e.g.: sub4/sess1/scan0/suite2p_0
Source code in element_calcium_imaging/imaging_preprocess.py 
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@classmethod
def infer_output_dir(cls, key, relative=False, mkdir=False):
    """Infer an output directory for an entry in ProcessingTask table.

    Args:
        key (dict): Primary key from the ProcessingTask table.
        relative (bool): If True, processing_output_dir is returned relative to
            imaging_root_dir. Default False.
        mkdir (bool): If True, create the processing_output_dir directory.
            Default True.

    Returns:
        dir (str): A default output directory for the processed results (processed_output_dir
            in ProcessingTask) based on the following convention:
            processed_dir / scan_dir / {processing_method}_{paramset_idx}
            e.g.: sub4/sess1/scan0/suite2p_0
    """
    acq_software = (scan.Scan & key).fetch1("acq_software")
    filetypes = dict(
        ScanImage="*.tif", Scanbox="*.sbx", NIS="*.nd2", PrairieView="*.tif"
    )

    scan_dir = find_full_path(
        get_imaging_root_data_dir(),
        get_calcium_imaging_files(key, filetypes[acq_software])[0],
    ).parent
    root_dir = find_root_directory(get_imaging_root_data_dir(), scan_dir)

    method = (
        (ProcessingParamSet & key).fetch1("processing_method").replace(".", "-")
    )

    processed_dir = pathlib.Path(get_processed_root_data_dir())
    output_dir = (
        processed_dir
        / scan_dir.relative_to(root_dir)
        / f'{method}_{key["paramset_idx"]}'
    )

    if mkdir:
        output_dir.mkdir(parents=True, exist_ok=True)

    return output_dir.relative_to(processed_dir) if relative else output_dir

generate(scan_key, paramset_idx=0) classmethod

Generate a ProcessingTask for a Scan using an parameter ProcessingParamSet

Generate an entry in the ProcessingTask table for a particular scan using an existing parameter set from the ProcessingParamSet table.

Parameters:

Name Type Description Default
scan_key dict

Primary key from Scan table.

 required
paramset_idx int

Unique parameter set ID.

 0
Source code in element_calcium_imaging/imaging_preprocess.py 
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@classmethod
def generate(cls, scan_key, paramset_idx=0):
    """Generate a ProcessingTask for a Scan using an parameter ProcessingParamSet

    Generate an entry in the ProcessingTask table for a particular scan using an
    existing parameter set from the ProcessingParamSet table.

    Args:
        scan_key (dict): Primary key from Scan table.
        paramset_idx (int): Unique parameter set ID.
    """
    key = {**scan_key, "paramset_idx": paramset_idx}

    processed_dir = get_processed_root_data_dir()
    output_dir = cls.infer_output_dir(key, relative=False, mkdir=True)

    method = (ProcessingParamSet & {"paramset_idx": paramset_idx}).fetch1(
        "processing_method"
    )

    try:
        if method == "suite2p":
            from element_interface import suite2p_loader

            suite2p_loader.Suite2p(output_dir)
        elif method == "caiman":
            from element_interface import caiman_loader

            caiman_loader.CaImAn(output_dir)
        elif method == "extract":
            from element_interface import extract_loader

            extract_loader.EXTRACT(output_dir)

        else:
            raise NotImplementedError(
                "Unknown/unimplemented method: {}".format(method)
            )
    except FileNotFoundError:
        task_mode = "trigger"
    else:
        task_mode = "load"

    cls.insert1(
        {
            **key,
            "processing_output_dir": output_dir.relative_to(
                processed_dir
            ).as_posix(),
            "task_mode": task_mode,
        }
    )

Processing

Bases: Computed

Perform the computation of an entry (task) defined in the ProcessingTask table. The computation is performed only on the scans with ScanInfo inserted.

Attributes:

Name Type Description
ProcessingTask foreign key

Primary key from ProcessingTask.
processing_time datetime

Process completion datetime.
package_version str

Version of the analysis package used in processing the data.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class Processing(dj.Computed):
    """Perform the computation of an entry (task) defined in the ProcessingTask table.
    The computation is performed only on the scans with ScanInfo inserted.

    Attributes:
        ProcessingTask (foreign key): Primary key from ProcessingTask.
        processing_time (datetime): Process completion datetime.
        package_version (str, optional): Version of the analysis package used in
            processing the data.
    """

    definition = """
    -> ProcessingTask
    ---
    processing_time     : datetime  # Time of generation of this set of processed, segmented results
    package_version=''  : varchar(16)
    """

    # Run processing only on Scan with ScanInfo inserted
    @property
    def key_source(self):
        """Limit the Processing to Scans that have their metadata ingested to the
        database."""

        return ProcessingTask & scan.ScanInfo

    def make(self, key):
        """Execute the calcium imaging analysis defined by the ProcessingTask."""

        task_mode, output_dir = (ProcessingTask & key).fetch1(
            "task_mode", "processing_output_dir"
        )

        if not output_dir:
            output_dir = ProcessingTask.infer_output_dir(key, relative=True, mkdir=True)
            # update processing_output_dir
            ProcessingTask.update1(
                {**key, "processing_output_dir": output_dir.as_posix()}
            )
        output_dir = find_full_path(get_imaging_root_data_dir(), output_dir).as_posix()

        if task_mode == "load":
            method, imaging_dataset = get_loader_result(key, ProcessingTask)
            if method == "suite2p":
                if (scan.ScanInfo & key).fetch1("nrois") > 0:
                    raise NotImplementedError(
                        "Suite2p ingestion error - Unable to handle"
                        + " ScanImage multi-ROI scanning mode yet"
                    )
                suite2p_dataset = imaging_dataset
                key = {**key, "processing_time": suite2p_dataset.creation_time}
            elif method == "caiman":
                caiman_dataset = imaging_dataset
                key = {**key, "processing_time": caiman_dataset.creation_time}
            elif method == "extract":
                raise NotImplementedError(
                    "To use EXTRACT with this DataJoint Element please set `task_mode=trigger`"
                )
            else:
                raise NotImplementedError("Unknown method: {}".format(method))
        elif task_mode == "trigger":
            method = (ProcessingParamSet * ProcessingTask & key).fetch1(
                "processing_method"
            )

            preprocess_paramsets = (
                PreprocessParamSteps.Step()
                & dict(preprocess_param_steps_id=key["preprocess_param_steps_id"])
            ).fetch("paramset_idx")

            if len(preprocess_paramsets) == 0:
                # No pre-processing steps were performed on the acquired dataset, so process the raw/acquired files.
                image_files = (scan.ScanInfo.ScanFile & key).fetch("file_path")
                image_files = [
                    find_full_path(get_imaging_root_data_dir(), image_file)
                    for image_file in image_files
                ]

            else:
                preprocess_output_dir = (PreprocessTask & key).fetch1(
                    "preprocess_output_dir"
                )

                preprocess_output_dir = find_full_path(
                    get_imaging_root_data_dir(), preprocess_output_dir
                )

                if not preprocess_output_dir.exists():
                    raise FileNotFoundError(
                        f"Pre-processed output directory not found ({preprocess_output_dir})"
                    )

                image_files = list(preprocess_output_dir.glob("*.tif"))

            if method == "suite2p":
                import suite2p

                suite2p_params = (ProcessingTask * ProcessingParamSet & key).fetch1(
                    "params"
                )
                suite2p_params["save_path0"] = output_dir
                (
                    suite2p_params["fs"],
                    suite2p_params["nplanes"],
                    suite2p_params["nchannels"],
                ) = (scan.ScanInfo & key).fetch1("fps", "ndepths", "nchannels")

                input_format = pathlib.Path(image_files[0]).suffix
                suite2p_params["input_format"] = input_format[1:]

                suite2p_paths = {
                    "data_path": [image_files[0].parent.as_posix()],
                    "tiff_list": [f.as_posix() for f in image_files],
                }

                suite2p.run_s2p(ops=suite2p_params, db=suite2p_paths)  # Run suite2p

                _, imaging_dataset = get_loader_result(key, ProcessingTask)
                suite2p_dataset = imaging_dataset
                key = {**key, "processing_time": suite2p_dataset.creation_time}

            elif method == "caiman":
                from element_interface.caiman_loader import _process_scanimage_tiff
                from element_interface.run_caiman import run_caiman

                caiman_params = (ProcessingTask * ProcessingParamSet & key).fetch1(
                    "params"
                )
                sampling_rate, ndepths, nchannels = (scan.ScanInfo & key).fetch1(
                    "fps", "ndepths", "nchannels"
                )

                is3D = bool(ndepths > 1)
                if is3D:
                    raise NotImplementedError(
                        "Caiman pipeline is not yet capable of analyzing 3D scans."
                    )

                # handle multi-channel tiff image before running CaImAn
                if nchannels > 1:
                    channel_idx = caiman_params.get("channel_to_process", 0)
                    tmp_dir = pathlib.Path(output_dir) / "channel_separated_tif"
                    tmp_dir.mkdir(exist_ok=True)
                    _process_scanimage_tiff(
                        [f.as_posix() for f in image_files], output_dir=tmp_dir
                    )
                    image_files = tmp_dir.glob(f"*_chn{channel_idx}.tif")

                run_caiman(
                    file_paths=[f.as_posix() for f in image_files],
                    parameters=caiman_params,
                    sampling_rate=sampling_rate,
                    output_dir=output_dir,
                    is3D=is3D,
                )

                _, imaging_dataset = get_loader_result(key, ProcessingTask)
                caiman_dataset = imaging_dataset
                key["processing_time"] = caiman_dataset.creation_time

            elif method == "extract":
                import suite2p
                from element_interface.extract_trigger import EXTRACT_trigger
                from scipy.io import savemat

                # Motion Correction with Suite2p
                params = (ProcessingTask * ProcessingParamSet & key).fetch1("params")

                params["suite2p"]["save_path0"] = output_dir
                (
                    params["suite2p"]["fs"],
                    params["suite2p"]["nplanes"],
                    params["suite2p"]["nchannels"],
                ) = (scan.ScanInfo & key).fetch1("fps", "ndepths", "nchannels")

                input_format = pathlib.Path(image_files[0]).suffix
                params["suite2p"]["input_format"] = input_format[1:]

                suite2p_paths = {
                    "data_path": [image_files[0].parent.as_posix()],
                    "tiff_list": [f.as_posix() for f in image_files],
                }

                suite2p.run_s2p(ops=params["suite2p"], db=suite2p_paths)

                # Convert data.bin to registered_scans.mat
                scanfile_fullpath = pathlib.Path(output_dir) / "suite2p/plane0/data.bin"

                data_shape = (scan.ScanInfo * scan.ScanInfo.Field & key).fetch1(
                    "nframes", "px_height", "px_width"
                )
                data = np.memmap(scanfile_fullpath, shape=data_shape, dtype=np.int16)

                scan_matlab_fullpath = scanfile_fullpath.parent / "registered_scan.mat"

                # Save the motion corrected movie (data.bin) in a .mat file
                savemat(
                    scan_matlab_fullpath,
                    {"M": np.transpose(data, axes=[1, 2, 0])},
                )

                # Execute EXTRACT

                ex = EXTRACT_trigger(
                    scan_matlab_fullpath, params["extract"], output_dir
                )
                ex.run()

                _, extract_dataset = get_loader_result(key, ProcessingTask)
                key["processing_time"] = extract_dataset.creation_time

        else:
            raise ValueError(f"Unknown task mode: {task_mode}")

        self.insert1({**key, "package_version": ""})

key_source property

Limit the Processing to Scans that have their metadata ingested to the database.

make(key)

Execute the calcium imaging analysis defined by the ProcessingTask.

Source code in element_calcium_imaging/imaging_preprocess.py 
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def make(self, key):
    """Execute the calcium imaging analysis defined by the ProcessingTask."""

    task_mode, output_dir = (ProcessingTask & key).fetch1(
        "task_mode", "processing_output_dir"
    )

    if not output_dir:
        output_dir = ProcessingTask.infer_output_dir(key, relative=True, mkdir=True)
        # update processing_output_dir
        ProcessingTask.update1(
            {**key, "processing_output_dir": output_dir.as_posix()}
        )
    output_dir = find_full_path(get_imaging_root_data_dir(), output_dir).as_posix()

    if task_mode == "load":
        method, imaging_dataset = get_loader_result(key, ProcessingTask)
        if method == "suite2p":
            if (scan.ScanInfo & key).fetch1("nrois") > 0:
                raise NotImplementedError(
                    "Suite2p ingestion error - Unable to handle"
                    + " ScanImage multi-ROI scanning mode yet"
                )
            suite2p_dataset = imaging_dataset
            key = {**key, "processing_time": suite2p_dataset.creation_time}
        elif method == "caiman":
            caiman_dataset = imaging_dataset
            key = {**key, "processing_time": caiman_dataset.creation_time}
        elif method == "extract":
            raise NotImplementedError(
                "To use EXTRACT with this DataJoint Element please set `task_mode=trigger`"
            )
        else:
            raise NotImplementedError("Unknown method: {}".format(method))
    elif task_mode == "trigger":
        method = (ProcessingParamSet * ProcessingTask & key).fetch1(
            "processing_method"
        )

        preprocess_paramsets = (
            PreprocessParamSteps.Step()
            & dict(preprocess_param_steps_id=key["preprocess_param_steps_id"])
        ).fetch("paramset_idx")

        if len(preprocess_paramsets) == 0:
            # No pre-processing steps were performed on the acquired dataset, so process the raw/acquired files.
            image_files = (scan.ScanInfo.ScanFile & key).fetch("file_path")
            image_files = [
                find_full_path(get_imaging_root_data_dir(), image_file)
                for image_file in image_files
            ]

        else:
            preprocess_output_dir = (PreprocessTask & key).fetch1(
                "preprocess_output_dir"
            )

            preprocess_output_dir = find_full_path(
                get_imaging_root_data_dir(), preprocess_output_dir
            )

            if not preprocess_output_dir.exists():
                raise FileNotFoundError(
                    f"Pre-processed output directory not found ({preprocess_output_dir})"
                )

            image_files = list(preprocess_output_dir.glob("*.tif"))

        if method == "suite2p":
            import suite2p

            suite2p_params = (ProcessingTask * ProcessingParamSet & key).fetch1(
                "params"
            )
            suite2p_params["save_path0"] = output_dir
            (
                suite2p_params["fs"],
                suite2p_params["nplanes"],
                suite2p_params["nchannels"],
            ) = (scan.ScanInfo & key).fetch1("fps", "ndepths", "nchannels")

            input_format = pathlib.Path(image_files[0]).suffix
            suite2p_params["input_format"] = input_format[1:]

            suite2p_paths = {
                "data_path": [image_files[0].parent.as_posix()],
                "tiff_list": [f.as_posix() for f in image_files],
            }

            suite2p.run_s2p(ops=suite2p_params, db=suite2p_paths)  # Run suite2p

            _, imaging_dataset = get_loader_result(key, ProcessingTask)
            suite2p_dataset = imaging_dataset
            key = {**key, "processing_time": suite2p_dataset.creation_time}

        elif method == "caiman":
            from element_interface.caiman_loader import _process_scanimage_tiff
            from element_interface.run_caiman import run_caiman

            caiman_params = (ProcessingTask * ProcessingParamSet & key).fetch1(
                "params"
            )
            sampling_rate, ndepths, nchannels = (scan.ScanInfo & key).fetch1(
                "fps", "ndepths", "nchannels"
            )

            is3D = bool(ndepths > 1)
            if is3D:
                raise NotImplementedError(
                    "Caiman pipeline is not yet capable of analyzing 3D scans."
                )

            # handle multi-channel tiff image before running CaImAn
            if nchannels > 1:
                channel_idx = caiman_params.get("channel_to_process", 0)
                tmp_dir = pathlib.Path(output_dir) / "channel_separated_tif"
                tmp_dir.mkdir(exist_ok=True)
                _process_scanimage_tiff(
                    [f.as_posix() for f in image_files], output_dir=tmp_dir
                )
                image_files = tmp_dir.glob(f"*_chn{channel_idx}.tif")

            run_caiman(
                file_paths=[f.as_posix() for f in image_files],
                parameters=caiman_params,
                sampling_rate=sampling_rate,
                output_dir=output_dir,
                is3D=is3D,
            )

            _, imaging_dataset = get_loader_result(key, ProcessingTask)
            caiman_dataset = imaging_dataset
            key["processing_time"] = caiman_dataset.creation_time

        elif method == "extract":
            import suite2p
            from element_interface.extract_trigger import EXTRACT_trigger
            from scipy.io import savemat

            # Motion Correction with Suite2p
            params = (ProcessingTask * ProcessingParamSet & key).fetch1("params")

            params["suite2p"]["save_path0"] = output_dir
            (
                params["suite2p"]["fs"],
                params["suite2p"]["nplanes"],
                params["suite2p"]["nchannels"],
            ) = (scan.ScanInfo & key).fetch1("fps", "ndepths", "nchannels")

            input_format = pathlib.Path(image_files[0]).suffix
            params["suite2p"]["input_format"] = input_format[1:]

            suite2p_paths = {
                "data_path": [image_files[0].parent.as_posix()],
                "tiff_list": [f.as_posix() for f in image_files],
            }

            suite2p.run_s2p(ops=params["suite2p"], db=suite2p_paths)

            # Convert data.bin to registered_scans.mat
            scanfile_fullpath = pathlib.Path(output_dir) / "suite2p/plane0/data.bin"

            data_shape = (scan.ScanInfo * scan.ScanInfo.Field & key).fetch1(
                "nframes", "px_height", "px_width"
            )
            data = np.memmap(scanfile_fullpath, shape=data_shape, dtype=np.int16)

            scan_matlab_fullpath = scanfile_fullpath.parent / "registered_scan.mat"

            # Save the motion corrected movie (data.bin) in a .mat file
            savemat(
                scan_matlab_fullpath,
                {"M": np.transpose(data, axes=[1, 2, 0])},
            )

            # Execute EXTRACT

            ex = EXTRACT_trigger(
                scan_matlab_fullpath, params["extract"], output_dir
            )
            ex.run()

            _, extract_dataset = get_loader_result(key, ProcessingTask)
            key["processing_time"] = extract_dataset.creation_time

    else:
        raise ValueError(f"Unknown task mode: {task_mode}")

    self.insert1({**key, "package_version": ""})

Curation

Bases: Manual

Curated results

If no curation is applied, the curation_output_dir can be set to the value of processing_output_dir.

Attributes:

Name Type Description
Processing foreign key

Primary key from Processing.
curation_id int

Unique curation ID.
curation_time datetime

Time of generation of this set of curated results.
curation_output_dir str

Output directory of the curated results, relative to root data directory.
manual_curation bool

If True, manual curation has been performed on this result.
curation_note str

Notes about the curation task.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class Curation(dj.Manual):
    """Curated results

    If no curation is applied, the curation_output_dir can be set to
    the value of processing_output_dir.

    Attributes:
        Processing (foreign key): Primary key from Processing.
        curation_id (int): Unique curation ID.
        curation_time (datetime): Time of generation of this set of curated results.
        curation_output_dir (str): Output directory of the curated results, relative to
            root data directory.
        manual_curation (bool): If True, manual curation has been performed on this
            result.
        curation_note (str, optional): Notes about the curation task.
    """

    definition = """# Curation(s) results
    -> Processing
    curation_id: int
    ---
    curation_time: datetime  # Time of generation of this set of curated results
    curation_output_dir: varchar(255)  # Output directory of the curated results, relative to root data directory
    manual_curation: bool  # Has manual curation been performed on this result?
    curation_note='': varchar(2000)
    """

    def create1_from_processing_task(self, key, is_curated=False, curation_note=""):
        """Create a Curation entry for a given ProcessingTask key.

        Args:
            key (dict): Primary key set of an entry in the ProcessingTask table.
            is_curated (bool): When True, indicates a manual curation.
            curation_note (str): User's note on the specifics of the curation.
        """
        if key not in Processing():
            raise ValueError(
                f"No corresponding entry in Processing available for: {key};"
                f"Please run `Processing.populate(key)`"
            )

        output_dir = (ProcessingTask & key).fetch1("processing_output_dir")
        method, imaging_dataset = get_loader_result(key, ProcessingTask)

        if method == "suite2p":
            suite2p_dataset = imaging_dataset
            curation_time = suite2p_dataset.creation_time
        elif method == "caiman":
            caiman_dataset = imaging_dataset
            curation_time = caiman_dataset.creation_time
        elif method == "extract":
            extract_dataset = imaging_dataset
            curation_time = extract_dataset.creation_time
        else:
            raise NotImplementedError("Unknown method: {}".format(method))

        # Synthesize curation_id
        curation_id = (
            dj.U().aggr(self & key, n="ifnull(max(curation_id)+1,1)").fetch1("n")
        )
        self.insert1(
            {
                **key,
                "curation_id": curation_id,
                "curation_time": curation_time,
                "curation_output_dir": output_dir,
                "manual_curation": is_curated,
                "curation_note": curation_note,
            }
        )

create1_from_processing_task(key, is_curated=False, curation_note='')

Create a Curation entry for a given ProcessingTask key.

Parameters:

Name Type Description Default
key dict

Primary key set of an entry in the ProcessingTask table.

 required
is_curated bool

When True, indicates a manual curation.

 False
curation_note str

User's note on the specifics of the curation.

 ''
Source code in element_calcium_imaging/imaging_preprocess.py 
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def create1_from_processing_task(self, key, is_curated=False, curation_note=""):
    """Create a Curation entry for a given ProcessingTask key.

    Args:
        key (dict): Primary key set of an entry in the ProcessingTask table.
        is_curated (bool): When True, indicates a manual curation.
        curation_note (str): User's note on the specifics of the curation.
    """
    if key not in Processing():
        raise ValueError(
            f"No corresponding entry in Processing available for: {key};"
            f"Please run `Processing.populate(key)`"
        )

    output_dir = (ProcessingTask & key).fetch1("processing_output_dir")
    method, imaging_dataset = get_loader_result(key, ProcessingTask)

    if method == "suite2p":
        suite2p_dataset = imaging_dataset
        curation_time = suite2p_dataset.creation_time
    elif method == "caiman":
        caiman_dataset = imaging_dataset
        curation_time = caiman_dataset.creation_time
    elif method == "extract":
        extract_dataset = imaging_dataset
        curation_time = extract_dataset.creation_time
    else:
        raise NotImplementedError("Unknown method: {}".format(method))

    # Synthesize curation_id
    curation_id = (
        dj.U().aggr(self & key, n="ifnull(max(curation_id)+1,1)").fetch1("n")
    )
    self.insert1(
        {
            **key,
            "curation_id": curation_id,
            "curation_time": curation_time,
            "curation_output_dir": output_dir,
            "manual_curation": is_curated,
            "curation_note": curation_note,
        }
    )

MotionCorrection

Bases: Imported

Results of motion correction shifts performed on the imaging data.

Attributes:

Name Type Description
Curation foreign key

Primary key from Curation.
scan.Channel.proj(motion_correct_channel='channel') int

Channel used for motion correction in this processing task.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class MotionCorrection(dj.Imported):
    """Results of motion correction shifts performed on the imaging data.

    Attributes:
        Curation (foreign key): Primary key from Curation.
        scan.Channel.proj(motion_correct_channel='channel') (int): Channel used for
            motion correction in this processing task.
    """

    definition = """# Results of motion correction
    -> Curation
    ---
    -> scan.Channel.proj(motion_correct_channel='channel') # channel used for motion correction in this processing task
    """

    class RigidMotionCorrection(dj.Part):
        """Details of rigid motion correction performed on the imaging data.

        Attributes:
            MotionCorrection (foreign key): Primary key from MotionCorrection.
            outlier_frames (longblob): Mask with true for frames with outlier shifts
                (already corrected).
            y_shifts (longblob): y motion correction shifts (pixels).
            x_shifts (longblob): x motion correction shifts (pixels).
            z_shifts (longblob, optional): z motion correction shifts (z-drift, pixels).
            y_std (float): standard deviation of y shifts across all frames (pixels).
            x_std (float): standard deviation of x shifts across all frames (pixels).
            z_std (float, optional): standard deviation of z shifts across all frames
                (pixels).
        """

        definition = """# Details of rigid motion correction performed on the imaging data
        -> master
        ---
        outlier_frames=null : longblob  # mask with true for frames with outlier shifts (already corrected)
        y_shifts            : longblob  # (pixels) y motion correction shifts
        x_shifts            : longblob  # (pixels) x motion correction shifts
        z_shifts=null       : longblob  # (pixels) z motion correction shifts (z-drift)
        y_std               : float     # (pixels) standard deviation of y shifts across all frames
        x_std               : float     # (pixels) standard deviation of x shifts across all frames
        z_std=null          : float     # (pixels) standard deviation of z shifts across all frames
        """

    class NonRigidMotionCorrection(dj.Part):
        """Piece-wise rigid motion correction - tile the FOV into multiple 3D
        blocks/patches.

        Attributes:
            MotionCorrection (foreign key): Primary key from MotionCorrection.
            outlier_frames (longblob, null): Mask with true for frames with outlier
                shifts (already corrected).
            block_height (int): Block height in pixels.
            block_width (int): Block width in pixels.
            block_depth (int): Block depth in pixels.
            block_count_y (int): Number of blocks tiled in the y direction.
            block_count_x (int): Number of blocks tiled in the x direction.
            block_count_z (int): Number of blocks tiled in the z direction.
        """

        definition = """# Details of non-rigid motion correction performed on the imaging data
        -> master
        ---
        outlier_frames=null : longblob # mask with true for frames with outlier shifts (already corrected)
        block_height        : int      # (pixels)
        block_width         : int      # (pixels)
        block_depth         : int      # (pixels)
        block_count_y       : int      # number of blocks tiled in the y direction
        block_count_x       : int      # number of blocks tiled in the x direction
        block_count_z       : int      # number of blocks tiled in the z direction
        """

    class Block(dj.Part):
        """FOV-tiled blocks used for non-rigid motion correction.

        Attributes:
            NonRigidMotionCorrection (foreign key): Primary key from
                NonRigidMotionCorrection.
            block_id (int): Unique block ID.
            block_y (longblob): y_start and y_end in pixels for this block
            block_x (longblob): x_start and x_end in pixels for this block
            block_z (longblob): z_start and z_end in pixels for this block
            y_shifts (longblob): y motion correction shifts for every frame in pixels
            x_shifts (longblob): x motion correction shifts for every frame in pixels
            z_shift=null (longblob, optional): x motion correction shifts for every frame
                in pixels
            y_std (float): standard deviation of y shifts across all frames in pixels
            x_std (float): standard deviation of x shifts across all frames in pixels
            z_std=null (float, optional): standard deviation of z shifts across all frames
                in pixels
        """

        definition = """# FOV-tiled blocks used for non-rigid motion correction
        -> master.NonRigidMotionCorrection
        block_id        : int
        ---
        block_y         : longblob  # (y_start, y_end) in pixel of this block
        block_x         : longblob  # (x_start, x_end) in pixel of this block
        block_z         : longblob  # (z_start, z_end) in pixel of this block
        y_shifts        : longblob  # (pixels) y motion correction shifts for every frame
        x_shifts        : longblob  # (pixels) x motion correction shifts for every frame
        z_shifts=null   : longblob  # (pixels) x motion correction shifts for every frame
        y_std           : float     # (pixels) standard deviation of y shifts across all frames
        x_std           : float     # (pixels) standard deviation of x shifts across all frames
        z_std=null      : float     # (pixels) standard deviation of z shifts across all frames
        """

    class Summary(dj.Part):
        """Summary images for each field and channel after corrections.

        Attributes:
            MotionCorrection (foreign key): Primary key from MotionCorrection.
            scan.ScanInfo.Field (foreign key): Primary key from scan.ScanInfo.Field.
            ref_image (longblob): Image used as alignment template.
            average_image (longblob): Mean of registered frames.
            correlation_image (longblob, optional): Correlation map (computed during
                cell detection).
            max_proj_image (longblob, optional): Max of registered frames.
        """

        definition = """# Summary images for each field and channel after corrections
        -> master
        -> scan.ScanInfo.Field
        ---
        ref_image               : longblob  # image used as alignment template
        average_image           : longblob  # mean of registered frames
        correlation_image=null  : longblob  # correlation map (computed during cell detection)
        max_proj_image=null     : longblob  # max of registered frames
        """

    def make(self, key):
        """Populate MotionCorrection with results parsed from analysis outputs"""

        method, imaging_dataset = get_loader_result(key, Curation)

        field_keys, _ = (scan.ScanInfo.Field & key).fetch(
            "KEY", "field_z", order_by="field_z"
        )

        if method in ["suite2p", "extract"]:
            suite2p_dataset = imaging_dataset

            motion_correct_channel = suite2p_dataset.planes[0].alignment_channel

            # ---- iterate through all s2p plane outputs ----
            rigid_correction, nonrigid_correction, nonrigid_blocks = {}, {}, {}
            summary_images = []
            for idx, (plane, s2p) in enumerate(suite2p_dataset.planes.items()):
                # -- rigid motion correction --
                if idx == 0:
                    rigid_correction = {
                        **key,
                        "y_shifts": s2p.ops["yoff"],
                        "x_shifts": s2p.ops["xoff"],
                        "z_shifts": np.full_like(s2p.ops["xoff"], 0),
                        "y_std": np.nanstd(s2p.ops["yoff"]),
                        "x_std": np.nanstd(s2p.ops["xoff"]),
                        "z_std": np.nan,
                        "outlier_frames": s2p.ops["badframes"],
                    }
                else:
                    rigid_correction["y_shifts"] = np.vstack(
                        [rigid_correction["y_shifts"], s2p.ops["yoff"]]
                    )
                    rigid_correction["y_std"] = np.nanstd(
                        rigid_correction["y_shifts"].flatten()
                    )
                    rigid_correction["x_shifts"] = np.vstack(
                        [rigid_correction["x_shifts"], s2p.ops["xoff"]]
                    )
                    rigid_correction["x_std"] = np.nanstd(
                        rigid_correction["x_shifts"].flatten()
                    )
                    rigid_correction["outlier_frames"] = np.logical_or(
                        rigid_correction["outlier_frames"], s2p.ops["badframes"]
                    )
                # -- non-rigid motion correction --
                if s2p.ops["nonrigid"]:
                    if idx == 0:
                        nonrigid_correction = {
                            **key,
                            "block_height": s2p.ops["block_size"][0],
                            "block_width": s2p.ops["block_size"][1],
                            "block_depth": 1,
                            "block_count_y": s2p.ops["nblocks"][0],
                            "block_count_x": s2p.ops["nblocks"][1],
                            "block_count_z": len(suite2p_dataset.planes),
                            "outlier_frames": s2p.ops["badframes"],
                        }
                    else:
                        nonrigid_correction["outlier_frames"] = np.logical_or(
                            nonrigid_correction["outlier_frames"],
                            s2p.ops["badframes"],
                        )
                    for b_id, (b_y, b_x, bshift_y, bshift_x) in enumerate(
                        zip(
                            s2p.ops["xblock"],
                            s2p.ops["yblock"],
                            s2p.ops["yoff1"].T,
                            s2p.ops["xoff1"].T,
                        )
                    ):
                        if b_id in nonrigid_blocks:
                            nonrigid_blocks[b_id]["y_shifts"] = np.vstack(
                                [nonrigid_blocks[b_id]["y_shifts"], bshift_y]
                            )
                            nonrigid_blocks[b_id]["y_std"] = np.nanstd(
                                nonrigid_blocks[b_id]["y_shifts"].flatten()
                            )
                            nonrigid_blocks[b_id]["x_shifts"] = np.vstack(
                                [nonrigid_blocks[b_id]["x_shifts"], bshift_x]
                            )
                            nonrigid_blocks[b_id]["x_std"] = np.nanstd(
                                nonrigid_blocks[b_id]["x_shifts"].flatten()
                            )
                        else:
                            nonrigid_blocks[b_id] = {
                                **key,
                                "block_id": b_id,
                                "block_y": b_y,
                                "block_x": b_x,
                                "block_z": np.full_like(b_x, plane),
                                "y_shifts": bshift_y,
                                "x_shifts": bshift_x,
                                "z_shifts": np.full(
                                    (
                                        len(suite2p_dataset.planes),
                                        len(bshift_x),
                                    ),
                                    0,
                                ),
                                "y_std": np.nanstd(bshift_y),
                                "x_std": np.nanstd(bshift_x),
                                "z_std": np.nan,
                            }

                # -- summary images --
                motion_correction_key = (
                    scan.ScanInfo.Field * Curation & key & field_keys[plane]
                ).fetch1("KEY")
                summary_images.append(
                    {
                        **motion_correction_key,
                        "ref_image": s2p.ref_image,
                        "average_image": s2p.mean_image,
                        "correlation_image": s2p.correlation_map,
                        "max_proj_image": s2p.max_proj_image,
                    }
                )

            self.insert1({**key, "motion_correct_channel": motion_correct_channel})
            if rigid_correction:
                self.RigidMotionCorrection.insert1(rigid_correction)
            if nonrigid_correction:
                self.NonRigidMotionCorrection.insert1(nonrigid_correction)
                self.Block.insert(nonrigid_blocks.values())
            self.Summary.insert(summary_images)
        elif method == "caiman":
            caiman_dataset = imaging_dataset

            self.insert1(
                {
                    **key,
                    "motion_correct_channel": caiman_dataset.alignment_channel,
                }
            )

            is3D = caiman_dataset.params.motion["is3D"]
            if not caiman_dataset.params.motion["pw_rigid"]:
                # -- rigid motion correction --
                rigid_correction = {
                    **key,
                    "x_shifts": caiman_dataset.motion_correction["shifts_rig"][:, 0],
                    "y_shifts": caiman_dataset.motion_correction["shifts_rig"][:, 1],
                    "z_shifts": (
                        caiman_dataset.motion_correction["shifts_rig"][:, 2]
                        if is3D
                        else np.full_like(
                            caiman_dataset.motion_correction["shifts_rig"][:, 0],
                            0,
                        )
                    ),
                    "x_std": np.nanstd(
                        caiman_dataset.motion_correction["shifts_rig"][:, 0]
                    ),
                    "y_std": np.nanstd(
                        caiman_dataset.motion_correction["shifts_rig"][:, 1]
                    ),
                    "z_std": (
                        np.nanstd(caiman_dataset.motion_correction["shifts_rig"][:, 2])
                        if is3D
                        else np.nan
                    ),
                    "outlier_frames": None,
                }

                self.RigidMotionCorrection.insert1(rigid_correction)
            else:
                # -- non-rigid motion correction --
                nonrigid_correction = {
                    **key,
                    "block_height": (
                        caiman_dataset.params.motion["strides"][0]
                        + caiman_dataset.params.motion["overlaps"][0]
                    ),
                    "block_width": (
                        caiman_dataset.params.motion["strides"][1]
                        + caiman_dataset.params.motion["overlaps"][1]
                    ),
                    "block_depth": (
                        caiman_dataset.params.motion["strides"][2]
                        + caiman_dataset.params.motion["overlaps"][2]
                        if is3D
                        else 1
                    ),
                    "block_count_x": len(
                        set(caiman_dataset.motion_correction["coord_shifts_els"][:, 0])
                    ),
                    "block_count_y": len(
                        set(caiman_dataset.motion_correction["coord_shifts_els"][:, 2])
                    ),
                    "block_count_z": (
                        len(
                            set(
                                caiman_dataset.motion_correction["coord_shifts_els"][
                                    :, 4
                                ]
                            )
                        )
                        if is3D
                        else 1
                    ),
                    "outlier_frames": None,
                }

                nonrigid_blocks = []
                for b_id in range(
                    len(caiman_dataset.motion_correction["x_shifts_els"][0, :])
                ):
                    nonrigid_blocks.append(
                        {
                            **key,
                            "block_id": b_id,
                            "block_x": np.arange(
                                *caiman_dataset.motion_correction["coord_shifts_els"][
                                    b_id, 0:2
                                ]
                            ),
                            "block_y": np.arange(
                                *caiman_dataset.motion_correction["coord_shifts_els"][
                                    b_id, 2:4
                                ]
                            ),
                            "block_z": (
                                np.arange(
                                    *caiman_dataset.motion_correction[
                                        "coord_shifts_els"
                                    ][b_id, 4:6]
                                )
                                if is3D
                                else np.full_like(
                                    np.arange(
                                        *caiman_dataset.motion_correction[
                                            "coord_shifts_els"
                                        ][b_id, 0:2]
                                    ),
                                    0,
                                )
                            ),
                            "x_shifts": caiman_dataset.motion_correction[
                                "x_shifts_els"
                            ][:, b_id],
                            "y_shifts": caiman_dataset.motion_correction[
                                "y_shifts_els"
                            ][:, b_id],
                            "z_shifts": (
                                caiman_dataset.motion_correction["z_shifts_els"][
                                    :, b_id
                                ]
                                if is3D
                                else np.full_like(
                                    caiman_dataset.motion_correction["x_shifts_els"][
                                        :, b_id
                                    ],
                                    0,
                                )
                            ),
                            "x_std": np.nanstd(
                                caiman_dataset.motion_correction["x_shifts_els"][
                                    :, b_id
                                ]
                            ),
                            "y_std": np.nanstd(
                                caiman_dataset.motion_correction["y_shifts_els"][
                                    :, b_id
                                ]
                            ),
                            "z_std": (
                                np.nanstd(
                                    caiman_dataset.motion_correction["z_shifts_els"][
                                        :, b_id
                                    ]
                                )
                                if is3D
                                else np.nan
                            ),
                        }
                    )

                self.NonRigidMotionCorrection.insert1(nonrigid_correction)
                self.Block.insert(nonrigid_blocks)

            # -- summary images --
            summary_images = [
                {
                    **key,
                    **fkey,
                    "ref_image": ref_image,
                    "average_image": ave_img,
                    "correlation_image": corr_img,
                    "max_proj_image": max_img,
                }
                for fkey, ref_image, ave_img, corr_img, max_img in zip(
                    field_keys,
                    caiman_dataset.motion_correction["reference_image"].transpose(
                        2, 0, 1
                    )
                    if is3D
                    else caiman_dataset.motion_correction["reference_image"][...][
                        np.newaxis, ...
                    ],
                    caiman_dataset.motion_correction["average_image"].transpose(2, 0, 1)
                    if is3D
                    else caiman_dataset.motion_correction["average_image"][...][
                        np.newaxis, ...
                    ],
                    caiman_dataset.motion_correction["correlation_image"].transpose(
                        2, 0, 1
                    )
                    if is3D
                    else caiman_dataset.motion_correction["correlation_image"][...][
                        np.newaxis, ...
                    ],
                    caiman_dataset.motion_correction["max_image"].transpose(2, 0, 1)
                    if is3D
                    else caiman_dataset.motion_correction["max_image"][...][
                        np.newaxis, ...
                    ],
                )
            ]
            self.Summary.insert(summary_images)
        else:
            raise NotImplementedError("Unknown/unimplemented method: {}".format(method))

RigidMotionCorrection

Bases: Part

Details of rigid motion correction performed on the imaging data.

Attributes:

Name Type Description
MotionCorrection foreign key

Primary key from MotionCorrection.
outlier_frames longblob

Mask with true for frames with outlier shifts (already corrected).
y_shifts longblob

y motion correction shifts (pixels).
x_shifts longblob

x motion correction shifts (pixels).
z_shifts longblob

z motion correction shifts (z-drift, pixels).
y_std float

standard deviation of y shifts across all frames (pixels).
x_std float

standard deviation of x shifts across all frames (pixels).
z_std float

standard deviation of z shifts across all frames (pixels).
Source code in element_calcium_imaging/imaging_preprocess.py 
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class RigidMotionCorrection(dj.Part):
    """Details of rigid motion correction performed on the imaging data.

    Attributes:
        MotionCorrection (foreign key): Primary key from MotionCorrection.
        outlier_frames (longblob): Mask with true for frames with outlier shifts
            (already corrected).
        y_shifts (longblob): y motion correction shifts (pixels).
        x_shifts (longblob): x motion correction shifts (pixels).
        z_shifts (longblob, optional): z motion correction shifts (z-drift, pixels).
        y_std (float): standard deviation of y shifts across all frames (pixels).
        x_std (float): standard deviation of x shifts across all frames (pixels).
        z_std (float, optional): standard deviation of z shifts across all frames
            (pixels).
    """

    definition = """# Details of rigid motion correction performed on the imaging data
    -> master
    ---
    outlier_frames=null : longblob  # mask with true for frames with outlier shifts (already corrected)
    y_shifts            : longblob  # (pixels) y motion correction shifts
    x_shifts            : longblob  # (pixels) x motion correction shifts
    z_shifts=null       : longblob  # (pixels) z motion correction shifts (z-drift)
    y_std               : float     # (pixels) standard deviation of y shifts across all frames
    x_std               : float     # (pixels) standard deviation of x shifts across all frames
    z_std=null          : float     # (pixels) standard deviation of z shifts across all frames
    """

NonRigidMotionCorrection

Bases: Part

Piece-wise rigid motion correction - tile the FOV into multiple 3D blocks/patches.

Attributes:

Name Type Description
MotionCorrection foreign key

Primary key from MotionCorrection.
outlier_frames (longblob, null)

Mask with true for frames with outlier shifts (already corrected).
block_height int

Block height in pixels.
block_width int

Block width in pixels.
block_depth int

Block depth in pixels.
block_count_y int

Number of blocks tiled in the y direction.
block_count_x int

Number of blocks tiled in the x direction.
block_count_z int

Number of blocks tiled in the z direction.
Source code in element_calcium_imaging/imaging_preprocess.py 
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class NonRigidMotionCorrection(dj.Part):
    """Piece-wise rigid motion correction - tile the FOV into multiple 3D
    blocks/patches.

    Attributes:
        MotionCorrection (foreign key): Primary key from MotionCorrection.
        outlier_frames (longblob, null): Mask with true for frames with outlier
            shifts (already corrected).
        block_height (int): Block height in pixels.
        block_width (int): Block width in pixels.
        block_depth (int): Block depth in pixels.
        block_count_y (int): Number of blocks tiled in the y direction.
        block_count_x (int): Number of blocks tiled in the x direction.
        block_count_z (int): Number of blocks tiled in the z direction.
    """

    definition = """# Details of non-rigid motion correction performed on the imaging data
    -> master
    ---
    outlier_frames=null : longblob # mask with true for frames with outlier shifts (already corrected)
    block_height        : int      # (pixels)
    block_width         : int      # (pixels)
    block_depth         : int      # (pixels)
    block_count_y       : int      # number of blocks tiled in the y direction
    block_count_x       : int      # number of blocks tiled in the x direction
    block_count_z       : int      # number of blocks tiled in the z direction
    """

Block

Bases: Part

FOV-tiled blocks used for non-rigid motion correction.

Attributes:

Name Type Description
NonRigidMotionCorrection foreign key

Primary key from NonRigidMotionCorrection.
block_id int

Unique block ID.
block_y longblob

y_start and y_end in pixels for this block
block_x longblob

x_start and x_end in pixels for this block
block_z longblob

z_start and z_end in pixels for this block
y_shifts longblob

y motion correction shifts for every frame in pixels
x_shifts longblob

x motion correction shifts for every frame in pixels
z_shift=null longblob

x motion correction shifts for every frame in pixels
y_std float

standard deviation of y shifts across all frames in pixels
x_std float

standard deviation of x shifts across all frames in pixels
z_std=null float

standard deviation of z shifts across all frames in pixels
Source code in element_calcium_imaging/imaging_preprocess.py 
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class Block(dj.Part):
    """FOV-tiled blocks used for non-rigid motion correction.

    Attributes:
        NonRigidMotionCorrection (foreign key): Primary key from
            NonRigidMotionCorrection.
        block_id (int): Unique block ID.
        block_y (longblob): y_start and y_end in pixels for this block
        block_x (longblob): x_start and x_end in pixels for this block
        block_z (longblob): z_start and z_end in pixels for this block
        y_shifts (longblob): y motion correction shifts for every frame in pixels
        x_shifts (longblob): x motion correction shifts for every frame in pixels
        z_shift=null (longblob, optional): x motion correction shifts for every frame
            in pixels
        y_std (float): standard deviation of y shifts across all frames in pixels
        x_std (float): standard deviation of x shifts across all frames in pixels
        z_std=null (float, optional): standard deviation of z shifts across all frames
            in pixels
    """

    definition = """# FOV-tiled blocks used for non-rigid motion correction
    -> master.NonRigidMotionCorrection
    block_id        : int
    ---
    block_y         : longblob  # (y_start, y_end) in pixel of this block
    block_x         : longblob  # (x_start, x_end) in pixel of this block
    block_z         : longblob  # (z_start, z_end) in pixel of this block
    y_shifts        : longblob  # (pixels) y motion correction shifts for every frame
    x_shifts        : longblob  # (pixels) x motion correction shifts for every frame
    z_shifts=null   : longblob  # (pixels) x motion correction shifts for every frame
    y_std           : float     # (pixels) standard deviation of y shifts across all frames
    x_std           : float     # (pixels) standard deviation of x shifts across all frames
    z_std=null      : float     # (pixels) standard deviation of z shifts across all frames
    """

Summary

Bases: Part

Summary images for each field and channel after corrections.

Attributes:

Name Type Description
MotionCorrection foreign key

Primary key from MotionCorrection.
scan.ScanInfo.Field foreign key

Primary key from scan.ScanInfo.Field.
ref_image longblob

Image used as alignment template.
average_image longblob

Mean of registered frames.
correlation_image longblob

Correlation map (computed during cell detection).
max_proj_image longblob

Max of registered frames.
Source code in element_calcium_imaging/imaging_preprocess.py 
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class Summary(dj.Part):
    """Summary images for each field and channel after corrections.

    Attributes:
        MotionCorrection (foreign key): Primary key from MotionCorrection.
        scan.ScanInfo.Field (foreign key): Primary key from scan.ScanInfo.Field.
        ref_image (longblob): Image used as alignment template.
        average_image (longblob): Mean of registered frames.
        correlation_image (longblob, optional): Correlation map (computed during
            cell detection).
        max_proj_image (longblob, optional): Max of registered frames.
    """

    definition = """# Summary images for each field and channel after corrections
    -> master
    -> scan.ScanInfo.Field
    ---
    ref_image               : longblob  # image used as alignment template
    average_image           : longblob  # mean of registered frames
    correlation_image=null  : longblob  # correlation map (computed during cell detection)
    max_proj_image=null     : longblob  # max of registered frames
    """

make(key)

Populate MotionCorrection with results parsed from analysis outputs

Source code in element_calcium_imaging/imaging_preprocess.py 
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def make(self, key):
    """Populate MotionCorrection with results parsed from analysis outputs"""

    method, imaging_dataset = get_loader_result(key, Curation)

    field_keys, _ = (scan.ScanInfo.Field & key).fetch(
        "KEY", "field_z", order_by="field_z"
    )

    if method in ["suite2p", "extract"]:
        suite2p_dataset = imaging_dataset

        motion_correct_channel = suite2p_dataset.planes[0].alignment_channel

        # ---- iterate through all s2p plane outputs ----
        rigid_correction, nonrigid_correction, nonrigid_blocks = {}, {}, {}
        summary_images = []
        for idx, (plane, s2p) in enumerate(suite2p_dataset.planes.items()):
            # -- rigid motion correction --
            if idx == 0:
                rigid_correction = {
                    **key,
                    "y_shifts": s2p.ops["yoff"],
                    "x_shifts": s2p.ops["xoff"],
                    "z_shifts": np.full_like(s2p.ops["xoff"], 0),
                    "y_std": np.nanstd(s2p.ops["yoff"]),
                    "x_std": np.nanstd(s2p.ops["xoff"]),
                    "z_std": np.nan,
                    "outlier_frames": s2p.ops["badframes"],
                }
            else:
                rigid_correction["y_shifts"] = np.vstack(
                    [rigid_correction["y_shifts"], s2p.ops["yoff"]]
                )
                rigid_correction["y_std"] = np.nanstd(
                    rigid_correction["y_shifts"].flatten()
                )
                rigid_correction["x_shifts"] = np.vstack(
                    [rigid_correction["x_shifts"], s2p.ops["xoff"]]
                )
                rigid_correction["x_std"] = np.nanstd(
                    rigid_correction["x_shifts"].flatten()
                )
                rigid_correction["outlier_frames"] = np.logical_or(
                    rigid_correction["outlier_frames"], s2p.ops["badframes"]
                )
            # -- non-rigid motion correction --
            if s2p.ops["nonrigid"]:
                if idx == 0:
                    nonrigid_correction = {
                        **key,
                        "block_height": s2p.ops["block_size"][0],
                        "block_width": s2p.ops["block_size"][1],
                        "block_depth": 1,
                        "block_count_y": s2p.ops["nblocks"][0],
                        "block_count_x": s2p.ops["nblocks"][1],
                        "block_count_z": len(suite2p_dataset.planes),
                        "outlier_frames": s2p.ops["badframes"],
                    }
                else:
                    nonrigid_correction["outlier_frames"] = np.logical_or(
                        nonrigid_correction["outlier_frames"],
                        s2p.ops["badframes"],
                    )
                for b_id, (b_y, b_x, bshift_y, bshift_x) in enumerate(
                    zip(
                        s2p.ops["xblock"],
                        s2p.ops["yblock"],
                        s2p.ops["yoff1"].T,
                        s2p.ops["xoff1"].T,
                    )
                ):
                    if b_id in nonrigid_blocks:
                        nonrigid_blocks[b_id]["y_shifts"] = np.vstack(
                            [nonrigid_blocks[b_id]["y_shifts"], bshift_y]
                        )
                        nonrigid_blocks[b_id]["y_std"] = np.nanstd(
                            nonrigid_blocks[b_id]["y_shifts"].flatten()
                        )
                        nonrigid_blocks[b_id]["x_shifts"] = np.vstack(
                            [nonrigid_blocks[b_id]["x_shifts"], bshift_x]
                        )
                        nonrigid_blocks[b_id]["x_std"] = np.nanstd(
                            nonrigid_blocks[b_id]["x_shifts"].flatten()
                        )
                    else:
                        nonrigid_blocks[b_id] = {
                            **key,
                            "block_id": b_id,
                            "block_y": b_y,
                            "block_x": b_x,
                            "block_z": np.full_like(b_x, plane),
                            "y_shifts": bshift_y,
                            "x_shifts": bshift_x,
                            "z_shifts": np.full(
                                (
                                    len(suite2p_dataset.planes),
                                    len(bshift_x),
                                ),
                                0,
                            ),
                            "y_std": np.nanstd(bshift_y),
                            "x_std": np.nanstd(bshift_x),
                            "z_std": np.nan,
                        }

            # -- summary images --
            motion_correction_key = (
                scan.ScanInfo.Field * Curation & key & field_keys[plane]
            ).fetch1("KEY")
            summary_images.append(
                {
                    **motion_correction_key,
                    "ref_image": s2p.ref_image,
                    "average_image": s2p.mean_image,
                    "correlation_image": s2p.correlation_map,
                    "max_proj_image": s2p.max_proj_image,
                }
            )

        self.insert1({**key, "motion_correct_channel": motion_correct_channel})
        if rigid_correction:
            self.RigidMotionCorrection.insert1(rigid_correction)
        if nonrigid_correction:
            self.NonRigidMotionCorrection.insert1(nonrigid_correction)
            self.Block.insert(nonrigid_blocks.values())
        self.Summary.insert(summary_images)
    elif method == "caiman":
        caiman_dataset = imaging_dataset

        self.insert1(
            {
                **key,
                "motion_correct_channel": caiman_dataset.alignment_channel,
            }
        )

        is3D = caiman_dataset.params.motion["is3D"]
        if not caiman_dataset.params.motion["pw_rigid"]:
            # -- rigid motion correction --
            rigid_correction = {
                **key,
                "x_shifts": caiman_dataset.motion_correction["shifts_rig"][:, 0],
                "y_shifts": caiman_dataset.motion_correction["shifts_rig"][:, 1],
                "z_shifts": (
                    caiman_dataset.motion_correction["shifts_rig"][:, 2]
                    if is3D
                    else np.full_like(
                        caiman_dataset.motion_correction["shifts_rig"][:, 0],
                        0,
                    )
                ),
                "x_std": np.nanstd(
                    caiman_dataset.motion_correction["shifts_rig"][:, 0]
                ),
                "y_std": np.nanstd(
                    caiman_dataset.motion_correction["shifts_rig"][:, 1]
                ),
                "z_std": (
                    np.nanstd(caiman_dataset.motion_correction["shifts_rig"][:, 2])
                    if is3D
                    else np.nan
                ),
                "outlier_frames": None,
            }

            self.RigidMotionCorrection.insert1(rigid_correction)
        else:
            # -- non-rigid motion correction --
            nonrigid_correction = {
                **key,
                "block_height": (
                    caiman_dataset.params.motion["strides"][0]
                    + caiman_dataset.params.motion["overlaps"][0]
                ),
                "block_width": (
                    caiman_dataset.params.motion["strides"][1]
                    + caiman_dataset.params.motion["overlaps"][1]
                ),
                "block_depth": (
                    caiman_dataset.params.motion["strides"][2]
                    + caiman_dataset.params.motion["overlaps"][2]
                    if is3D
                    else 1
                ),
                "block_count_x": len(
                    set(caiman_dataset.motion_correction["coord_shifts_els"][:, 0])
                ),
                "block_count_y": len(
                    set(caiman_dataset.motion_correction["coord_shifts_els"][:, 2])
                ),
                "block_count_z": (
                    len(
                        set(
                            caiman_dataset.motion_correction["coord_shifts_els"][
                                :, 4
                            ]
                        )
                    )
                    if is3D
                    else 1
                ),
                "outlier_frames": None,
            }

            nonrigid_blocks = []
            for b_id in range(
                len(caiman_dataset.motion_correction["x_shifts_els"][0, :])
            ):
                nonrigid_blocks.append(
                    {
                        **key,
                        "block_id": b_id,
                        "block_x": np.arange(
                            *caiman_dataset.motion_correction["coord_shifts_els"][
                                b_id, 0:2
                            ]
                        ),
                        "block_y": np.arange(
                            *caiman_dataset.motion_correction["coord_shifts_els"][
                                b_id, 2:4
                            ]
                        ),
                        "block_z": (
                            np.arange(
                                *caiman_dataset.motion_correction[
                                    "coord_shifts_els"
                                ][b_id, 4:6]
                            )
                            if is3D
                            else np.full_like(
                                np.arange(
                                    *caiman_dataset.motion_correction[
                                        "coord_shifts_els"
                                    ][b_id, 0:2]
                                ),
                                0,
                            )
                        ),
                        "x_shifts": caiman_dataset.motion_correction[
                            "x_shifts_els"
                        ][:, b_id],
                        "y_shifts": caiman_dataset.motion_correction[
                            "y_shifts_els"
                        ][:, b_id],
                        "z_shifts": (
                            caiman_dataset.motion_correction["z_shifts_els"][
                                :, b_id
                            ]
                            if is3D
                            else np.full_like(
                                caiman_dataset.motion_correction["x_shifts_els"][
                                    :, b_id
                                ],
                                0,
                            )
                        ),
                        "x_std": np.nanstd(
                            caiman_dataset.motion_correction["x_shifts_els"][
                                :, b_id
                            ]
                        ),
                        "y_std": np.nanstd(
                            caiman_dataset.motion_correction["y_shifts_els"][
                                :, b_id
                            ]
                        ),
                        "z_std": (
                            np.nanstd(
                                caiman_dataset.motion_correction["z_shifts_els"][
                                    :, b_id
                                ]
                            )
                            if is3D
                            else np.nan
                        ),
                    }
                )

            self.NonRigidMotionCorrection.insert1(nonrigid_correction)
            self.Block.insert(nonrigid_blocks)

        # -- summary images --
        summary_images = [
            {
                **key,
                **fkey,
                "ref_image": ref_image,
                "average_image": ave_img,
                "correlation_image": corr_img,
                "max_proj_image": max_img,
            }
            for fkey, ref_image, ave_img, corr_img, max_img in zip(
                field_keys,
                caiman_dataset.motion_correction["reference_image"].transpose(
                    2, 0, 1
                )
                if is3D
                else caiman_dataset.motion_correction["reference_image"][...][
                    np.newaxis, ...
                ],
                caiman_dataset.motion_correction["average_image"].transpose(2, 0, 1)
                if is3D
                else caiman_dataset.motion_correction["average_image"][...][
                    np.newaxis, ...
                ],
                caiman_dataset.motion_correction["correlation_image"].transpose(
                    2, 0, 1
                )
                if is3D
                else caiman_dataset.motion_correction["correlation_image"][...][
                    np.newaxis, ...
                ],
                caiman_dataset.motion_correction["max_image"].transpose(2, 0, 1)
                if is3D
                else caiman_dataset.motion_correction["max_image"][...][
                    np.newaxis, ...
                ],
            )
        ]
        self.Summary.insert(summary_images)
    else:
        raise NotImplementedError("Unknown/unimplemented method: {}".format(method))

Segmentation

Bases: Computed

Result of the Segmentation process.

Attributes:

Name Type Description
Curation foreign key

Primary key from Curation.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class Segmentation(dj.Computed):
    """Result of the Segmentation process.

    Attributes:
        Curation (foreign key): Primary key from Curation.
    """

    definition = """# Different mask segmentations.
    -> Curation
    """

    class Mask(dj.Part):
        """Details of the masks identified from the Segmentation procedure.

        Attributes:
            Segmentation (foreign key): Primary key from Segmentation.
            mask (int): Unique mask ID.
            scan.Channel.proj(segmentation_channel='channel') (foreign key): Channel
                used for segmentation.
            mask_npix (int): Number of pixels in ROIs.
            mask_center_x (int): Center x coordinate in pixel.
            mask_center_y (int): Center y coordinate in pixel.
            mask_center_z (int): Center z coordinate in pixel.
            mask_xpix (longblob): X coordinates in pixels.
            mask_ypix (longblob): Y coordinates in pixels.
            mask_zpix (longblob): Z coordinates in pixels.
            mask_weights (longblob): Weights of the mask at the indices above.
        """

        definition = """ # A mask produced by segmentation.
        -> master
        mask               : smallint
        ---
        -> scan.Channel.proj(segmentation_channel='channel')  # channel used for segmentation
        mask_npix          : int       # number of pixels in ROIs
        mask_center_x      : int       # center x coordinate in pixel
        mask_center_y      : int       # center y coordinate in pixel
        mask_center_z=null : int       # center z coordinate in pixel
        mask_xpix          : longblob  # x coordinates in pixels
        mask_ypix          : longblob  # y coordinates in pixels
        mask_zpix=null     : longblob  # z coordinates in pixels
        mask_weights       : longblob  # weights of the mask at the indices above
        """

    def make(self, key):
        """Populate the Segmentation with the results parsed from analysis outputs."""

        method, imaging_dataset = get_loader_result(key, Curation)

        if method == "suite2p":
            suite2p_dataset = imaging_dataset

            # ---- iterate through all s2p plane outputs ----
            masks, cells = [], []
            for plane, s2p in suite2p_dataset.planes.items():
                mask_count = len(masks)  # increment mask id from all "plane"
                for mask_idx, (is_cell, cell_prob, mask_stat) in enumerate(
                    zip(s2p.iscell, s2p.cell_prob, s2p.stat)
                ):
                    masks.append(
                        {
                            **key,
                            "mask": mask_idx + mask_count,
                            "segmentation_channel": s2p.segmentation_channel,
                            "mask_npix": mask_stat["npix"],
                            "mask_center_x": mask_stat["med"][1],
                            "mask_center_y": mask_stat["med"][0],
                            "mask_center_z": mask_stat.get("iplane", plane),
                            "mask_xpix": mask_stat["xpix"],
                            "mask_ypix": mask_stat["ypix"],
                            "mask_zpix": np.full(
                                mask_stat["npix"],
                                mask_stat.get("iplane", plane),
                            ),
                            "mask_weights": mask_stat["lam"],
                        }
                    )
                    if is_cell:
                        cells.append(
                            {
                                **key,
                                "mask_classification_method": "suite2p_default_classifier",
                                "mask": mask_idx + mask_count,
                                "mask_type": "soma",
                                "confidence": cell_prob,
                            }
                        )

            self.insert1(key)
            self.Mask.insert(masks, ignore_extra_fields=True)

            if cells:
                MaskClassification.insert1(
                    {
                        **key,
                        "mask_classification_method": "suite2p_default_classifier",
                    },
                    allow_direct_insert=True,
                )
                MaskClassification.MaskType.insert(
                    cells, ignore_extra_fields=True, allow_direct_insert=True
                )
        elif method == "caiman":
            caiman_dataset = imaging_dataset

            # infer "segmentation_channel" - from params if available, else from caiman loader
            params = (ProcessingParamSet * ProcessingTask & key).fetch1("params")
            segmentation_channel = params.get(
                "segmentation_channel", caiman_dataset.segmentation_channel
            )

            masks, cells = [], []
            for mask in caiman_dataset.masks:
                masks.append(
                    {
                        **key,
                        "segmentation_channel": segmentation_channel,
                        "mask": mask["mask_id"],
                        "mask_npix": mask["mask_npix"],
                        "mask_center_x": mask["mask_center_x"],
                        "mask_center_y": mask["mask_center_y"],
                        "mask_center_z": mask["mask_center_z"],
                        "mask_xpix": mask["mask_xpix"],
                        "mask_ypix": mask["mask_ypix"],
                        "mask_zpix": mask["mask_zpix"],
                        "mask_weights": mask["mask_weights"],
                    }
                )
                if caiman_dataset.cnmf.estimates.idx_components is not None:
                    if mask["mask_id"] in caiman_dataset.cnmf.estimates.idx_components:
                        cells.append(
                            {
                                **key,
                                "mask_classification_method": "caiman_default_classifier",
                                "mask": mask["mask_id"],
                                "mask_type": "soma",
                            }
                        )

            self.insert1(key)
            self.Mask.insert(masks, ignore_extra_fields=True)

            if cells:
                MaskClassification.insert1(
                    {
                        **key,
                        "mask_classification_method": "caiman_default_classifier",
                    },
                    allow_direct_insert=True,
                )
                MaskClassification.MaskType.insert(
                    cells, ignore_extra_fields=True, allow_direct_insert=True
                )
        elif method == "extract":
            extract_dataset = imaging_dataset
            masks = [
                dict(
                    **key,
                    segmentation_channel=0,
                    mask=mask["mask_id"],
                    mask_npix=mask["mask_npix"],
                    mask_center_x=mask["mask_center_x"],
                    mask_center_y=mask["mask_center_y"],
                    mask_center_z=mask["mask_center_z"],
                    mask_xpix=mask["mask_xpix"],
                    mask_ypix=mask["mask_ypix"],
                    mask_zpix=mask["mask_zpix"],
                    mask_weights=mask["mask_weights"],
                )
                for mask in extract_dataset.load_results()
            ]

            self.insert1(key)
            self.Mask.insert(masks, ignore_extra_fields=True)
        else:
            raise NotImplementedError(f"Unknown/unimplemented method: {method}")

Mask

Bases: Part

Details of the masks identified from the Segmentation procedure.

Attributes:

Name Type Description
Segmentation foreign key

Primary key from Segmentation.
mask int

Unique mask ID.
scan.Channel.proj(segmentation_channel='channel') foreign key

Channel used for segmentation.
mask_npix int

Number of pixels in ROIs.
mask_center_x int

Center x coordinate in pixel.
mask_center_y int

Center y coordinate in pixel.
mask_center_z int

Center z coordinate in pixel.
mask_xpix longblob

X coordinates in pixels.
mask_ypix longblob

Y coordinates in pixels.
mask_zpix longblob

Z coordinates in pixels.
mask_weights longblob

Weights of the mask at the indices above.
Source code in element_calcium_imaging/imaging_preprocess.py 
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class Mask(dj.Part):
    """Details of the masks identified from the Segmentation procedure.

    Attributes:
        Segmentation (foreign key): Primary key from Segmentation.
        mask (int): Unique mask ID.
        scan.Channel.proj(segmentation_channel='channel') (foreign key): Channel
            used for segmentation.
        mask_npix (int): Number of pixels in ROIs.
        mask_center_x (int): Center x coordinate in pixel.
        mask_center_y (int): Center y coordinate in pixel.
        mask_center_z (int): Center z coordinate in pixel.
        mask_xpix (longblob): X coordinates in pixels.
        mask_ypix (longblob): Y coordinates in pixels.
        mask_zpix (longblob): Z coordinates in pixels.
        mask_weights (longblob): Weights of the mask at the indices above.
    """

    definition = """ # A mask produced by segmentation.
    -> master
    mask               : smallint
    ---
    -> scan.Channel.proj(segmentation_channel='channel')  # channel used for segmentation
    mask_npix          : int       # number of pixels in ROIs
    mask_center_x      : int       # center x coordinate in pixel
    mask_center_y      : int       # center y coordinate in pixel
    mask_center_z=null : int       # center z coordinate in pixel
    mask_xpix          : longblob  # x coordinates in pixels
    mask_ypix          : longblob  # y coordinates in pixels
    mask_zpix=null     : longblob  # z coordinates in pixels
    mask_weights       : longblob  # weights of the mask at the indices above
    """

make(key)

Populate the Segmentation with the results parsed from analysis outputs.

Source code in element_calcium_imaging/imaging_preprocess.py 
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def make(self, key):
    """Populate the Segmentation with the results parsed from analysis outputs."""

    method, imaging_dataset = get_loader_result(key, Curation)

    if method == "suite2p":
        suite2p_dataset = imaging_dataset

        # ---- iterate through all s2p plane outputs ----
        masks, cells = [], []
        for plane, s2p in suite2p_dataset.planes.items():
            mask_count = len(masks)  # increment mask id from all "plane"
            for mask_idx, (is_cell, cell_prob, mask_stat) in enumerate(
                zip(s2p.iscell, s2p.cell_prob, s2p.stat)
            ):
                masks.append(
                    {
                        **key,
                        "mask": mask_idx + mask_count,
                        "segmentation_channel": s2p.segmentation_channel,
                        "mask_npix": mask_stat["npix"],
                        "mask_center_x": mask_stat["med"][1],
                        "mask_center_y": mask_stat["med"][0],
                        "mask_center_z": mask_stat.get("iplane", plane),
                        "mask_xpix": mask_stat["xpix"],
                        "mask_ypix": mask_stat["ypix"],
                        "mask_zpix": np.full(
                            mask_stat["npix"],
                            mask_stat.get("iplane", plane),
                        ),
                        "mask_weights": mask_stat["lam"],
                    }
                )
                if is_cell:
                    cells.append(
                        {
                            **key,
                            "mask_classification_method": "suite2p_default_classifier",
                            "mask": mask_idx + mask_count,
                            "mask_type": "soma",
                            "confidence": cell_prob,
                        }
                    )

        self.insert1(key)
        self.Mask.insert(masks, ignore_extra_fields=True)

        if cells:
            MaskClassification.insert1(
                {
                    **key,
                    "mask_classification_method": "suite2p_default_classifier",
                },
                allow_direct_insert=True,
            )
            MaskClassification.MaskType.insert(
                cells, ignore_extra_fields=True, allow_direct_insert=True
            )
    elif method == "caiman":
        caiman_dataset = imaging_dataset

        # infer "segmentation_channel" - from params if available, else from caiman loader
        params = (ProcessingParamSet * ProcessingTask & key).fetch1("params")
        segmentation_channel = params.get(
            "segmentation_channel", caiman_dataset.segmentation_channel
        )

        masks, cells = [], []
        for mask in caiman_dataset.masks:
            masks.append(
                {
                    **key,
                    "segmentation_channel": segmentation_channel,
                    "mask": mask["mask_id"],
                    "mask_npix": mask["mask_npix"],
                    "mask_center_x": mask["mask_center_x"],
                    "mask_center_y": mask["mask_center_y"],
                    "mask_center_z": mask["mask_center_z"],
                    "mask_xpix": mask["mask_xpix"],
                    "mask_ypix": mask["mask_ypix"],
                    "mask_zpix": mask["mask_zpix"],
                    "mask_weights": mask["mask_weights"],
                }
            )
            if caiman_dataset.cnmf.estimates.idx_components is not None:
                if mask["mask_id"] in caiman_dataset.cnmf.estimates.idx_components:
                    cells.append(
                        {
                            **key,
                            "mask_classification_method": "caiman_default_classifier",
                            "mask": mask["mask_id"],
                            "mask_type": "soma",
                        }
                    )

        self.insert1(key)
        self.Mask.insert(masks, ignore_extra_fields=True)

        if cells:
            MaskClassification.insert1(
                {
                    **key,
                    "mask_classification_method": "caiman_default_classifier",
                },
                allow_direct_insert=True,
            )
            MaskClassification.MaskType.insert(
                cells, ignore_extra_fields=True, allow_direct_insert=True
            )
    elif method == "extract":
        extract_dataset = imaging_dataset
        masks = [
            dict(
                **key,
                segmentation_channel=0,
                mask=mask["mask_id"],
                mask_npix=mask["mask_npix"],
                mask_center_x=mask["mask_center_x"],
                mask_center_y=mask["mask_center_y"],
                mask_center_z=mask["mask_center_z"],
                mask_xpix=mask["mask_xpix"],
                mask_ypix=mask["mask_ypix"],
                mask_zpix=mask["mask_zpix"],
                mask_weights=mask["mask_weights"],
            )
            for mask in extract_dataset.load_results()
        ]

        self.insert1(key)
        self.Mask.insert(masks, ignore_extra_fields=True)
    else:
        raise NotImplementedError(f"Unknown/unimplemented method: {method}")

MaskClassificationMethod

Bases: Lookup

Available mask classification methods.

Attributes:

Name Type Description
mask_classification_method str

Mask classification method.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class MaskClassificationMethod(dj.Lookup):
    """Available mask classification methods.

    Attributes:
        mask_classification_method (str): Mask classification method.
    """

    definition = """
    mask_classification_method: varchar(48)
    """

    contents = zip(["suite2p_default_classifier", "caiman_default_classifier"])

MaskClassification

Bases: Computed

Classes assigned to each mask.

Attributes:

Name Type Description
Segmentation foreign key

Primary key from Segmentation.
MaskClassificationMethod foreign key

Primary key from MaskClassificationMethod.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class MaskClassification(dj.Computed):
    """Classes assigned to each mask.

    Attributes:
        Segmentation (foreign key): Primary key from Segmentation.
        MaskClassificationMethod (foreign key): Primary key from
            MaskClassificationMethod.
    """

    definition = """
    -> Segmentation
    -> MaskClassificationMethod
    """

    class MaskType(dj.Part):
        """Type assigned to each mask.

        Attributes:
            MaskClassification (foreign key): Primary key from MaskClassification.
            Segmentation.Mask (foreign key): Primary key from Segmentation.Mask.
            MaskType: Primary key from MaskType.
            confidence (float, optional): Confidence level of the mask classification.
        """

        definition = """
        -> master
        -> Segmentation.Mask
        ---
        -> MaskType
        confidence=null: float
        """

    def make(self, key):
        pass

MaskType

Bases: Part

Type assigned to each mask.

Attributes:

Name Type Description
MaskClassification foreign key

Primary key from MaskClassification.
Segmentation.Mask foreign key

Primary key from Segmentation.Mask.
MaskType foreign key

Primary key from MaskType.
confidence float

Confidence level of the mask classification.
Source code in element_calcium_imaging/imaging_preprocess.py 
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class MaskType(dj.Part):
    """Type assigned to each mask.

    Attributes:
        MaskClassification (foreign key): Primary key from MaskClassification.
        Segmentation.Mask (foreign key): Primary key from Segmentation.Mask.
        MaskType: Primary key from MaskType.
        confidence (float, optional): Confidence level of the mask classification.
    """

    definition = """
    -> master
    -> Segmentation.Mask
    ---
    -> MaskType
    confidence=null: float
    """

Fluorescence

Bases: Computed

Fluorescence traces.

Attributes:

Name Type Description
Segmentation foreign key

Primary key from Segmentation.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class Fluorescence(dj.Computed):
    """Fluorescence traces.

    Attributes:
        Segmentation (foreign key): Primary key from Segmentation.
    """

    definition = """# Fluorescence traces before spike extraction or filtering
    -> Segmentation
    """

    class Trace(dj.Part):
        """Traces obtained from segmented region of interests.

        Attributes:
            Fluorescence (foreign key): Primary key from Fluorescence.
            Segmentation.Mask (foreign key): Primary key from Segmentation.Mask.
            scan.Channel.proj(fluo_channel='channel') (int): The channel that this trace
                comes from.
            fluorescence (longblob): Fluorescence trace associated with this mask.
            neuropil_fluorescence (longblob, optional): Neuropil fluorescence trace.
        """

        definition = """
        -> master
        -> Segmentation.Mask
        -> scan.Channel.proj(fluo_channel='channel')  # The channel that this trace comes from
        ---
        fluorescence                : longblob  # Fluorescence trace associated with this mask
        neuropil_fluorescence=null  : longblob  # Neuropil fluorescence trace
        """

    def make(self, key):
        """Populate the Fluorescence with the results parsed from analysis outputs."""

        method, imaging_dataset = get_loader_result(key, Curation)

        if method == "suite2p":
            suite2p_dataset = imaging_dataset

            # ---- iterate through all s2p plane outputs ----
            fluo_traces, fluo_chn2_traces = [], []
            for s2p in suite2p_dataset.planes.values():
                mask_count = len(fluo_traces)  # increment mask id from all "plane"
                for mask_idx, (f, fneu) in enumerate(zip(s2p.F, s2p.Fneu)):
                    fluo_traces.append(
                        {
                            **key,
                            "mask": mask_idx + mask_count,
                            "fluo_channel": 0,
                            "fluorescence": f,
                            "neuropil_fluorescence": fneu,
                        }
                    )
                if len(s2p.F_chan2):
                    mask_chn2_count = len(
                        fluo_chn2_traces
                    )  # increment mask id from all planes
                    for mask_idx, (f2, fneu2) in enumerate(
                        zip(s2p.F_chan2, s2p.Fneu_chan2)
                    ):
                        fluo_chn2_traces.append(
                            {
                                **key,
                                "mask": mask_idx + mask_chn2_count,
                                "fluo_channel": 1,
                                "fluorescence": f2,
                                "neuropil_fluorescence": fneu2,
                            }
                        )

            self.insert1(key)
            self.Trace.insert(fluo_traces + fluo_chn2_traces)
        elif method == "caiman":
            caiman_dataset = imaging_dataset

            # infer "segmentation_channel" - from params if available, else from caiman loader
            params = (ProcessingParamSet * ProcessingTask & key).fetch1("params")
            segmentation_channel = params.get(
                "segmentation_channel", caiman_dataset.segmentation_channel
            )

            fluo_traces = []
            for mask in caiman_dataset.masks:
                fluo_traces.append(
                    {
                        **key,
                        "mask": mask["mask_id"],
                        "fluo_channel": segmentation_channel,
                        "fluorescence": mask["inferred_trace"],
                    }
                )

            self.insert1(key)
            self.Trace.insert(fluo_traces)
        elif method == "extract":
            extract_dataset = imaging_dataset

            fluo_traces = [
                {
                    **key,
                    "mask": mask_id,
                    "fluo_channel": 0,
                    "fluorescence": fluorescence,
                }
                for mask_id, fluorescence in enumerate(extract_dataset.T)
            ]

            self.insert1(key)
            self.Trace.insert(fluo_traces)

        else:
            raise NotImplementedError("Unknown/unimplemented method: {}".format(method))

Trace

Bases: Part

Traces obtained from segmented region of interests.

Attributes:

Name Type Description
Fluorescence foreign key

Primary key from Fluorescence.
Segmentation.Mask foreign key

Primary key from Segmentation.Mask.
scan.Channel.proj(fluo_channel='channel') int

The channel that this trace comes from.
fluorescence longblob

Fluorescence trace associated with this mask.
neuropil_fluorescence longblob

Neuropil fluorescence trace.
Source code in element_calcium_imaging/imaging_preprocess.py 
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class Trace(dj.Part):
    """Traces obtained from segmented region of interests.

    Attributes:
        Fluorescence (foreign key): Primary key from Fluorescence.
        Segmentation.Mask (foreign key): Primary key from Segmentation.Mask.
        scan.Channel.proj(fluo_channel='channel') (int): The channel that this trace
            comes from.
        fluorescence (longblob): Fluorescence trace associated with this mask.
        neuropil_fluorescence (longblob, optional): Neuropil fluorescence trace.
    """

    definition = """
    -> master
    -> Segmentation.Mask
    -> scan.Channel.proj(fluo_channel='channel')  # The channel that this trace comes from
    ---
    fluorescence                : longblob  # Fluorescence trace associated with this mask
    neuropil_fluorescence=null  : longblob  # Neuropil fluorescence trace
    """

make(key)

Populate the Fluorescence with the results parsed from analysis outputs.

Source code in element_calcium_imaging/imaging_preprocess.py 
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def make(self, key):
    """Populate the Fluorescence with the results parsed from analysis outputs."""

    method, imaging_dataset = get_loader_result(key, Curation)

    if method == "suite2p":
        suite2p_dataset = imaging_dataset

        # ---- iterate through all s2p plane outputs ----
        fluo_traces, fluo_chn2_traces = [], []
        for s2p in suite2p_dataset.planes.values():
            mask_count = len(fluo_traces)  # increment mask id from all "plane"
            for mask_idx, (f, fneu) in enumerate(zip(s2p.F, s2p.Fneu)):
                fluo_traces.append(
                    {
                        **key,
                        "mask": mask_idx + mask_count,
                        "fluo_channel": 0,
                        "fluorescence": f,
                        "neuropil_fluorescence": fneu,
                    }
                )
            if len(s2p.F_chan2):
                mask_chn2_count = len(
                    fluo_chn2_traces
                )  # increment mask id from all planes
                for mask_idx, (f2, fneu2) in enumerate(
                    zip(s2p.F_chan2, s2p.Fneu_chan2)
                ):
                    fluo_chn2_traces.append(
                        {
                            **key,
                            "mask": mask_idx + mask_chn2_count,
                            "fluo_channel": 1,
                            "fluorescence": f2,
                            "neuropil_fluorescence": fneu2,
                        }
                    )

        self.insert1(key)
        self.Trace.insert(fluo_traces + fluo_chn2_traces)
    elif method == "caiman":
        caiman_dataset = imaging_dataset

        # infer "segmentation_channel" - from params if available, else from caiman loader
        params = (ProcessingParamSet * ProcessingTask & key).fetch1("params")
        segmentation_channel = params.get(
            "segmentation_channel", caiman_dataset.segmentation_channel
        )

        fluo_traces = []
        for mask in caiman_dataset.masks:
            fluo_traces.append(
                {
                    **key,
                    "mask": mask["mask_id"],
                    "fluo_channel": segmentation_channel,
                    "fluorescence": mask["inferred_trace"],
                }
            )

        self.insert1(key)
        self.Trace.insert(fluo_traces)
    elif method == "extract":
        extract_dataset = imaging_dataset

        fluo_traces = [
            {
                **key,
                "mask": mask_id,
                "fluo_channel": 0,
                "fluorescence": fluorescence,
            }
            for mask_id, fluorescence in enumerate(extract_dataset.T)
        ]

        self.insert1(key)
        self.Trace.insert(fluo_traces)

    else:
        raise NotImplementedError("Unknown/unimplemented method: {}".format(method))

ActivityExtractionMethod

Bases: Lookup

Available activity extraction methods.

Attributes:

Name Type Description
extraction_method str

Extraction method.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class ActivityExtractionMethod(dj.Lookup):
    """Available activity extraction methods.

    Attributes:
        extraction_method (str): Extraction method.
    """

    definition = """# Activity extraction method
    extraction_method: varchar(32)
    """

    contents = zip(["suite2p_deconvolution", "caiman_deconvolution", "caiman_dff"])

Activity

Bases: Computed

Inferred neural activity from fluorescence trace (e.g. dff, spikes, etc.).

Attributes:

Name Type Description
Fluorescence foreign key

Primary key from Fluorescence.
ActivityExtractionMethod foreign key

Primary key from ActivityExtractionMethod.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class Activity(dj.Computed):
    """Inferred neural activity from fluorescence trace (e.g. dff, spikes, etc.).

    Attributes:
        Fluorescence (foreign key): Primary key from Fluorescence.
        ActivityExtractionMethod (foreign key): Primary key from
            ActivityExtractionMethod.
    """

    definition = """# Neural Activity
    -> Fluorescence
    -> ActivityExtractionMethod
    """

    class Trace(dj.Part):
        """Trace(s) for each mask.

        Attributes:
            Activity (foreign key): Primary key from Activity.
            Fluorescence.Trace (foreign key): Fluorescence.Trace.
            activity_trace (longblob): Neural activity from fluorescence trace.
        """

        definition = """
        -> master
        -> Fluorescence.Trace
        ---
        activity_trace: longblob
        """

    @property
    def key_source(self):
        suite2p_key_source = (
            Fluorescence
            * ActivityExtractionMethod
            * ProcessingParamSet.proj("processing_method")
            & 'processing_method = "suite2p"'
            & 'extraction_method LIKE "suite2p%"'
        )
        caiman_key_source = (
            Fluorescence
            * ActivityExtractionMethod
            * ProcessingParamSet.proj("processing_method")
            & 'processing_method = "caiman"'
            & 'extraction_method LIKE "caiman%"'
        )
        return suite2p_key_source.proj() + caiman_key_source.proj()

    def make(self, key):
        """Populate the Activity with the results parsed from analysis outputs."""

        method, imaging_dataset = get_loader_result(key, Curation)

        if method == "suite2p":
            if key["extraction_method"] == "suite2p_deconvolution":
                suite2p_dataset = imaging_dataset
                # ---- iterate through all s2p plane outputs ----
                spikes = [
                    dict(
                        key,
                        mask=mask_idx,
                        fluo_channel=0,
                        activity_trace=spks,
                    )
                    for mask_idx, spks in enumerate(
                        s
                        for plane in suite2p_dataset.planes.values()
                        for s in plane.spks
                    )
                ]

                self.insert1(key)
                self.Trace.insert(spikes)
        elif method == "caiman":
            caiman_dataset = imaging_dataset

            if key["extraction_method"] in (
                "caiman_deconvolution",
                "caiman_dff",
            ):
                attr_mapper = {
                    "caiman_deconvolution": "spikes",
                    "caiman_dff": "dff",
                }

                # infer "segmentation_channel" - from params if available, else from caiman loader
                params = (ProcessingParamSet * ProcessingTask & key).fetch1("params")
                segmentation_channel = params.get(
                    "segmentation_channel", caiman_dataset.segmentation_channel
                )

                self.insert1(key)
                self.Trace.insert(
                    dict(
                        key,
                        mask=mask["mask_id"],
                        fluo_channel=segmentation_channel,
                        activity_trace=mask[attr_mapper[key["extraction_method"]]],
                    )
                    for mask in caiman_dataset.masks
                )
        else:
            raise NotImplementedError("Unknown/unimplemented method: {}".format(method))

Trace

Bases: Part

Trace(s) for each mask.

Attributes:

Name Type Description
Activity foreign key

Primary key from Activity.
Fluorescence.Trace foreign key

Fluorescence.Trace.
activity_trace longblob

Neural activity from fluorescence trace.
Source code in element_calcium_imaging/imaging_preprocess.py 
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class Trace(dj.Part):
    """Trace(s) for each mask.

    Attributes:
        Activity (foreign key): Primary key from Activity.
        Fluorescence.Trace (foreign key): Fluorescence.Trace.
        activity_trace (longblob): Neural activity from fluorescence trace.
    """

    definition = """
    -> master
    -> Fluorescence.Trace
    ---
    activity_trace: longblob
    """

make(key)

Populate the Activity with the results parsed from analysis outputs.

Source code in element_calcium_imaging/imaging_preprocess.py 
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def make(self, key):
    """Populate the Activity with the results parsed from analysis outputs."""

    method, imaging_dataset = get_loader_result(key, Curation)

    if method == "suite2p":
        if key["extraction_method"] == "suite2p_deconvolution":
            suite2p_dataset = imaging_dataset
            # ---- iterate through all s2p plane outputs ----
            spikes = [
                dict(
                    key,
                    mask=mask_idx,
                    fluo_channel=0,
                    activity_trace=spks,
                )
                for mask_idx, spks in enumerate(
                    s
                    for plane in suite2p_dataset.planes.values()
                    for s in plane.spks
                )
            ]

            self.insert1(key)
            self.Trace.insert(spikes)
    elif method == "caiman":
        caiman_dataset = imaging_dataset

        if key["extraction_method"] in (
            "caiman_deconvolution",
            "caiman_dff",
        ):
            attr_mapper = {
                "caiman_deconvolution": "spikes",
                "caiman_dff": "dff",
            }

            # infer "segmentation_channel" - from params if available, else from caiman loader
            params = (ProcessingParamSet * ProcessingTask & key).fetch1("params")
            segmentation_channel = params.get(
                "segmentation_channel", caiman_dataset.segmentation_channel
            )

            self.insert1(key)
            self.Trace.insert(
                dict(
                    key,
                    mask=mask["mask_id"],
                    fluo_channel=segmentation_channel,
                    activity_trace=mask[attr_mapper[key["extraction_method"]]],
                )
                for mask in caiman_dataset.masks
            )
    else:
        raise NotImplementedError("Unknown/unimplemented method: {}".format(method))

ProcessingQualityMetrics

Bases: Computed

Quality metrics used to evaluate the results of the calcium imaging analysis pipeline.

Attributes:

Name Type Description
Fluorescence foreign key

Primary key from Fluorescence.
Source code in element_calcium_imaging/imaging_preprocess.py 
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@schema
class ProcessingQualityMetrics(dj.Computed):
    """Quality metrics used to evaluate the results of the calcium imaging analysis pipeline.

    Attributes:
        Fluorescence (foreign key): Primary key from Fluorescence.
    """

    definition = """
    -> Fluorescence
    """

    class Mask(dj.Part):
        """Quality metrics used to evaluate the masks.

        Attributes:
            Fluorescence (foreign key): Primary key from Fluorescence.
            Segmentation.Mask (foreign key): Primary key from Segmentation.Mask.
            mask_area (float): Mask area in square micrometer.
            roundness (float): Roundness between 0 and 1. Values closer to 1 are rounder.
        """

        definition = """
        -> master
        -> Segmentation.Mask
        ---
        mask_area=null: float  # Mask area in square micrometer.
        roundness: float       # Roundness between 0 and 1. Values closer to 1 are rounder.
        """

    class Trace(dj.Part):
        """Quality metrics used to evaluate the fluorescence traces.

        Attributes:
            Fluorescence (foreign key): Primary key from Fluorescence.
            Fluorescence.Trace (foreign key): Primary key from Fluorescence.Trace.
            skewness (float): Skewness of the fluorescence trace.
            variance (float): Variance of the fluorescence trace.
        """

        definition = """
        -> master
        -> Fluorescence.Trace
        ---
        skewness: float   # Skewness of the fluorescence trace.
        variance: float   # Variance of the fluorescence trace.
        """

    def make(self, key):
        """Populate the ProcessingQualityMetrics table and its part tables."""
        from scipy.stats import skew

        (
            mask_xpixs,
            mask_ypixs,
            mask_weights,
            fluorescence,
            fluo_channels,
            mask_ids,
            mask_npix,
            px_height,
            px_width,
            um_height,
            um_width,
        ) = (Segmentation.Mask * scan.ScanInfo.Field * Fluorescence.Trace & key).fetch(
            "mask_xpix",
            "mask_ypix",
            "mask_weights",
            "fluorescence",
            "fluo_channel",
            "mask",
            "mask_npix",
            "px_height",
            "px_width",
            "um_height",
            "um_width",
        )

        norm_mean = lambda x: x.mean() / x.max()
        roundnesses = [
            norm_mean(np.linalg.eigvals(np.cov(x, y, aweights=w)))
            for x, y, w in zip(mask_xpixs, mask_ypixs, mask_weights)
        ]

        fluorescence = np.stack(fluorescence)

        self.insert1(key)

        self.Mask.insert(
            dict(key, mask=mask_id, mask_area=mask_area, roundness=roundness)
            for mask_id, mask_area, roundness in zip(
                mask_ids,
                mask_npix * (um_height / px_height) * (um_width / px_width),
                roundnesses,
            )
        )

        self.Trace.insert(
            dict(
                key,
                fluo_channel=fluo_channel,
                mask=mask_id,
                skewness=skewness,
                variance=variance,
            )
            for fluo_channel, mask_id, skewness, variance in zip(
                fluo_channels,
                mask_ids,
                skew(fluorescence, axis=1),
                fluorescence.std(axis=1),
            )
        )

Mask

Bases: Part

Quality metrics used to evaluate the masks.

Attributes:

Name Type Description
Fluorescence foreign key

Primary key from Fluorescence.
Segmentation.Mask foreign key

Primary key from Segmentation.Mask.
mask_area float

Mask area in square micrometer.
roundness float

Roundness between 0 and 1. Values closer to 1 are rounder.
Source code in element_calcium_imaging/imaging_preprocess.py 
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class Mask(dj.Part):
    """Quality metrics used to evaluate the masks.

    Attributes:
        Fluorescence (foreign key): Primary key from Fluorescence.
        Segmentation.Mask (foreign key): Primary key from Segmentation.Mask.
        mask_area (float): Mask area in square micrometer.
        roundness (float): Roundness between 0 and 1. Values closer to 1 are rounder.
    """

    definition = """
    -> master
    -> Segmentation.Mask
    ---
    mask_area=null: float  # Mask area in square micrometer.
    roundness: float       # Roundness between 0 and 1. Values closer to 1 are rounder.
    """

Trace

Bases: Part

Quality metrics used to evaluate the fluorescence traces.

Attributes:

Name Type Description
Fluorescence foreign key

Primary key from Fluorescence.
Fluorescence.Trace foreign key

Primary key from Fluorescence.Trace.
skewness float

Skewness of the fluorescence trace.
variance float

Variance of the fluorescence trace.
Source code in element_calcium_imaging/imaging_preprocess.py 
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class Trace(dj.Part):
    """Quality metrics used to evaluate the fluorescence traces.

    Attributes:
        Fluorescence (foreign key): Primary key from Fluorescence.
        Fluorescence.Trace (foreign key): Primary key from Fluorescence.Trace.
        skewness (float): Skewness of the fluorescence trace.
        variance (float): Variance of the fluorescence trace.
    """

    definition = """
    -> master
    -> Fluorescence.Trace
    ---
    skewness: float   # Skewness of the fluorescence trace.
    variance: float   # Variance of the fluorescence trace.
    """

make(key)

Populate the ProcessingQualityMetrics table and its part tables.

Source code in element_calcium_imaging/imaging_preprocess.py 
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def make(self, key):
    """Populate the ProcessingQualityMetrics table and its part tables."""
    from scipy.stats import skew

    (
        mask_xpixs,
        mask_ypixs,
        mask_weights,
        fluorescence,
        fluo_channels,
        mask_ids,
        mask_npix,
        px_height,
        px_width,
        um_height,
        um_width,
    ) = (Segmentation.Mask * scan.ScanInfo.Field * Fluorescence.Trace & key).fetch(
        "mask_xpix",
        "mask_ypix",
        "mask_weights",
        "fluorescence",
        "fluo_channel",
        "mask",
        "mask_npix",
        "px_height",
        "px_width",
        "um_height",
        "um_width",
    )

    norm_mean = lambda x: x.mean() / x.max()
    roundnesses = [
        norm_mean(np.linalg.eigvals(np.cov(x, y, aweights=w)))
        for x, y, w in zip(mask_xpixs, mask_ypixs, mask_weights)
    ]

    fluorescence = np.stack(fluorescence)

    self.insert1(key)

    self.Mask.insert(
        dict(key, mask=mask_id, mask_area=mask_area, roundness=roundness)
        for mask_id, mask_area, roundness in zip(
            mask_ids,
            mask_npix * (um_height / px_height) * (um_width / px_width),
            roundnesses,
        )
    )

    self.Trace.insert(
        dict(
            key,
            fluo_channel=fluo_channel,
            mask=mask_id,
            skewness=skewness,
            variance=variance,
        )
        for fluo_channel, mask_id, skewness, variance in zip(
            fluo_channels,
            mask_ids,
            skew(fluorescence, axis=1),
            fluorescence.std(axis=1),
        )
    )

get_loader_result(key, table)

Retrieve the processed imaging results from a suite2p or caiman loader.

Parameters:

Name Type Description Default
key dict

The key to one entry of ProcessingTask or Curation

 required
table Table

A datajoint table to retrieve the loaded results from (e.g. ProcessingTask, Curation)

 required

Raises:

Type Description
NotImplementedError

If the processing_method is different than 'suite2p' or 'caiman'.

Returns:

Type Description
Callable

A loader object of the loaded results (e.g. suite2p.Suite2p or caiman.CaImAn,
Callable

see element-interface for more information on the loaders.)
Source code in element_calcium_imaging/imaging_preprocess.py 
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def get_loader_result(key: dict, table: dj.Table) -> Callable:
    """Retrieve the processed imaging results from a suite2p or caiman loader.

    Args:
        key (dict): The `key` to one entry of ProcessingTask or Curation
        table (dj.Table): A datajoint table to retrieve the loaded results from (e.g.
            ProcessingTask, Curation)

    Raises:
        NotImplementedError: If the processing_method is different than 'suite2p' or
            'caiman'.

    Returns:
        A loader object of the loaded results (e.g. suite2p.Suite2p or caiman.CaImAn,
        see element-interface for more information on the loaders.)
    """
    method, output_dir = (ProcessingParamSet * table & key).fetch1(
        "processing_method", _table_attribute_mapper[table.__name__]
    )

    output_path = find_full_path(get_imaging_root_data_dir(), output_dir)

    if method == "suite2p" or (
        method == "extract" and table.__name__ == "MotionCorrection"
    ):
        from element_interface import suite2p_loader

        loaded_dataset = suite2p_loader.Suite2p(output_path)
    elif method == "caiman":
        from element_interface import caiman_loader

        loaded_dataset = caiman_loader.CaImAn(output_path)
    elif method == "extract":
        from element_interface import extract_loader

        loaded_dataset = extract_loader.EXTRACT(output_path)
    else:
        raise NotImplementedError("Unknown/unimplemented method: {}".format(method))

    return method, loaded_dataset